CN109149559A - A kind of Demand-side interconnection reliability estimation method and system - Google Patents
A kind of Demand-side interconnection reliability estimation method and system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The present invention relates to a kind of Demand-side interconnection reliability estimation method and systems, including the system mode computing system trend obtained based on sampling；Judge to calculate whether the system load flow obtained meets constraint condition；If satisfied, then returning to previous step continues computing system trend, until frequency in sampling reaches preset frequency in sampling；If not satisfied, then carrying out cutting load processing to Demand-side, and with the minimum target computing system trend of cutting load amount；Calculated result based on system load flow calculates the Model in Reliability Evaluation of Power Systems index under Demand-side interconnection, is based on the reliability assessment index, assesses the Power System Reliability under Demand-side interconnection.It realizes through the above scheme and the more accurate reliability assessment of effect is interconnected to Demand-side.
Description
Technical field
The present invention relates to Demand-side resource interconnection reliability assessment technical field more particularly to a kind of interconnections of Demand-side resource
Reliability estimation method and system.
Background technique
As social economy is fast-developing and technological progress, Demand-side distributed generation resource, electric car, distributed energy storage,
The user sides controllable resources rapid growth such as air-conditioning and boilers heated electrically, is greatly enriched the class of user side demand response controllable resources
Type and capacity.The interconnection strategies of current needs side resource are more polynary, therefore the exemplary requirement side resource under multi-constraint condition is mutual
Connection policy validation is current difficulties urgently to be resolved, wherein reasonability of the Demand-side interconnection effect reliability assessment to strategy
And ensure that electric system normal operation is particularly important when interconnection.But since Demand-side interconnection is related to a variety of Demand-side resources and sets
Standby, ambiguity and uncontrollability are stronger, and existing reliability index does not have specific aim.
Summary of the invention
In order to solve the deficiencies in the prior art, the present invention provides a kind of Demand-side resource interconnection reliability estimation method and is
System, the effect for Demand-side resource interconnection strategies provide reference, further improve electric system energy in the interconnection of Demand-side resource
The judgment criteria of enough safe and reliable operations.
In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical scheme that:
A kind of Demand-side interconnection reliability estimation method, which comprises
The system mode computing system trend obtained based on sampling；
Judge to calculate whether the system load flow obtained meets constraint condition；Continue to calculate system if satisfied, then returning to previous step
System trend, until the frequency in sampling reaches preset frequency in sampling；If not satisfied, then being carried out at cutting load to Demand-side
Reason, and with the minimum target computing system trend of cutting load amount；
Based on the calculated result of the system load flow, the Model in Reliability Evaluation of Power Systems index under Demand-side interconnection is calculated,
Based on the reliability assessment index, the Power System Reliability under Demand-side interconnection is assessed.
Preferably, the sampling acquisition system mode includes:
Using non-sequential Monte Carlo method, each equipment state in simulation system is uniformly distributed on [0,1] by section；
Random sampling is carried out to equipment state each in the system, forms system mode；
Using sampling frequency as the unbiased esti-mator of system mode probability, the system mode is determined；
Wherein, each equipment state includes: element operating status, line status and Demand-side resource power output shape in the system
State.
Further, system mode is determined by following formula:
S=(s1,s2,...,si,...,sN)
In formula, S indicates system mode, and N is that random number extracts number.
Further, pass through each equipment state in following formula simulation system:
In formula, siThe state for indicating element i, if si=0, then element i is in running order, if si=1, then element i is in
Malfunction, λiIndicate the failure rate of element i, RiThe equally distributed random number on [0,1] generated for element i.
Further, the unbiased esti-mator of system mode probability is determined by following formula:
In formula, M is frequency in sampling, and m (s) is the sampling frequency of system mode s.
Preferably, the system mode computing system trend obtained based on sampling includes: to define the system mode
For the initial value of Newton-Raphson approach, system load flow is calculated using Newton-Raphson approach.
Preferably, the constraint condition includes that there is no out-of-limit for node voltage under current system conditions and branch power.
Preferably, the calculated result based on system load flow, the Power System Reliability calculated under Demand-side interconnection are commented
Estimating index includes:
According to the calculated result of system mode and the system load flow, alternative capacity, the failure of Demand-side resource are determined
Energy loss amount, grid side load peak-valley difference, user side electricity needs and Network congestion risk afterwards.
Further, the alternative capacity of demand response resource is determined by following formula:
SDR(t)=D (t) × β
In formula, D (t) is electricity needs, SDR(t)、Respectively Demand-side resource and Demand-side resource is alternative
Capacity；β and β1Respectively Demand-side resource permeability and demand response substitution of resources coefficient.
Further, the energy loss amount after failure is determined by following formula:
In formula, X is energy loss amount of the user after grid collapses, t0For the time that failure starts, tfFor failure knot
The time that beam restores electricity, P are that user side is nonserviceabled the electricity demand in the duration.
Further, grid side load peak-valley difference is determined by following formula:
δ=LMAX-LMIN
In formula: δ is network load peak-valley difference, LMAXAnd LMINRespectively indicate peak load in the malfunction duration with
Minimum load.
Further, user side electricity needs is determined by following formula:
In formula: δ is network load peak-valley difference, and D (t) indicates user side electricity needs；Δ D (t) indicates that user side electric power needs
Seek increment；T is the time to be predicted；νGDPFor GDP growth rate；ε1、ε2Respectively electricity elasticity coefficients and price elasticity of electricity demand
Coefficient；P1For sales rate of electricity.
Further, Network congestion risk is determined by following formula:
In formula,For Network congestion risk, P2For T-D tariff, Cmar,capFor marginal Capacity Cost.
A kind of Demand-side interconnection reliability evaluation system, the system comprises:
Decimation blocks, the system mode computing system trend for being obtained based on sampling；
Determining module calculates whether the system load flow obtained meets constraint condition for judging；If satisfied, then returning to upper one
Step continues computing system trend, until the frequency in sampling reaches preset frequency in sampling；If not satisfied, then to Demand-side
Cutting load processing is carried out, and with the minimum target computing system trend of cutting load amount；
Evaluation module calculates the Power System Reliability under Demand-side interconnection for the calculated result based on system load flow
Evaluation index is based on the reliability assessment index, assesses the Power System Reliability under Demand-side interconnection.
Preferably, the decimation blocks include:
Simulation submodule is uniformly distributed simulation system on [0,1] by section for using non-sequential Monte Carlo method
Each equipment state in uniting；
Random sampling submodule forms system mode for carrying out random sampling to equipment state each in the system；
Submodule is analyzed, for determining the system mode using sampling frequency as the unbiased esti-mator of system mode probability.
Further, the random sampling submodule includes: generation unit, for determining system mode by following formula:
S=(s1,s2,...,si,...,sN)
In formula, S indicates system mode, and N is that random number extracts number.
Further, the simulation submodule includes: state simulation unit, for being set by each in following formula simulation system
Standby state:
In formula, siThe state for indicating element i, if si=0, then element i is in running order, if si=1, then element i is in
Malfunction, λiIndicate the failure rate of element i, RiThe equally distributed random number on [0,1] generated for element i.
Further, the analysis submodule comprises determining that unit, for determining the nothing of system mode probability by following formula
Estimation partially:
In formula, M is frequency in sampling, and m (s) is the sampling frequency of system mode s.
Further, the evaluation module includes: index estimation submodule, for according to system mode and system tide
The calculated result of stream determines energy loss amount after the alternative capacity of Demand-side resource, failure, grid side load peak-valley difference, uses
Family side electricity needs and Network congestion risk.
Further, the index estimates submodule, including the first computing unit, for determining demand response by following formula
The alternative capacity of resource:
SDR(t)=D (t) × β
In formula, D (t) is electricity needs, SDR(t)、Respectively Demand-side resource and Demand-side resource is alternative
Capacity；β and β1Respectively Demand-side resource permeability and demand response substitution of resources coefficient；
Second computing unit, for determining the energy loss amount after failure by following formula:
In formula, X is energy loss amount of the user after grid collapses, t0For the time that failure starts, tfFor failure knot
The time that beam restores electricity, P are that user side is nonserviceabled the electricity demand in the duration；
Third computing unit, for determining grid side load peak-valley difference by following formula:
δ=LMAX-LMIN
In formula: δ is network load peak-valley difference, LMAXAnd LMINRespectively indicate peak load in the malfunction duration with
Minimum load；
4th computing unit, for determining user side electricity needs by following formula:
In formula: δ is network load peak-valley difference, and D (t) indicates user side electricity needs；Δ D (t) indicates that user side electric power needs
Seek increment；T is the time to be predicted；νGDPFor GDP growth rate；ε1、ε2Respectively electricity elasticity coefficients and price elasticity of electricity demand
Coefficient；P1For sales rate of electricity；
5th computing unit, for determining Network congestion risk by following formula:
In formula,For Network congestion risk, P2For T-D tariff, Cmar,capFor marginal Capacity Cost.
Compared with the immediate prior art, the invention has the benefit that
A kind of Demand-side resource interconnection reliability estimation method proposed by the present invention and system, the system obtained based on sampling
State computation system load flow；Judge to calculate whether the system load flow obtained meets constraint condition；If satisfied, then return previous step after
Continuous computing system trend, until frequency in sampling reaches preset frequency in sampling；If not satisfied, then cut to Demand-side negative
Lotus processing, and with the minimum objective function computing system trend of cutting load amount；The system mode that sampling provided by the invention obtains
The operating condition that the method for computing system trend is in large scale suitable for electric system, interconnection resources and equipment are more complex, effectively save
About simulation time promotes calculating speed and efficiency, compensates for conventional method and needs a large amount of trend operations, the defect that time-consuming.It will
Cutting load is introduced into the calculating process of computing system trend and complies with user side electricity needs, not only fast convergence rate, so that reliably
Property assessment result can integrate different conditions Corrective control and restore control process, thus enhance obtain reliability assessment index
Reasonability and validity.
Finally based on the calculated result of system load flow, the Model in Reliability Evaluation of Power Systems index under Demand-side interconnection is calculated,
Based on the reliability assessment index, the Power System Reliability under Demand-side interconnection is assessed.Based on reliability assessment
Calculate, can accurate simulation Demand-side interconnect the lower various malfunctions of electric system, keep result more accurate.It realizes mutual to Demand-side
Join lower Model in Reliability Evaluation of Power Systems to calculate, to grasp the operational reliability of electric system and interconnection resources and equipment, there is needle
To property Model in Reliability Evaluation of Power Systems method is improved, and is different degrees of suffered by the lower electric system of Demand-side interconnection
It influences to provide the foundation of risk control and repair based on condition of component.
Detailed description of the invention
Fig. 1 is the Demand-side resource interconnection reliability estimation method flow chart provided in the embodiment of the present invention；
Fig. 2 is a kind of Demand-side interconnection reliability estimation method specific steps flow chart provided in the embodiment of the present invention.
Specific embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
Current Model in Reliability Evaluation of Power Systems technology mainly has analytic method and Monte Carlo method.Wherein analytic method mathematical logic
Clearly, model accuracy is high, fast for the calculating speed of mini-system, but with the increase of system scale, calculation amount can be in
Exponential growth, for some reliability indexs, there is likely to be difficulties, thus are more suitable for the simple electric power of small scale, structure
System.Monte Carlo method can be divided into three kinds of classifications according to the difference of principle: state sampling technology (non-sequential Monte Carlo emulation),
State shifts sampling techniques, state duration sampling techniques (sequential Monte Carlo emulation).Under certain required precision, cover
The frequency in sampling of special Caro method is unrelated with the scale of system, can eliminate the problem of analytic calculation amount sharp increase, can be adapted for
The reliability assessment of large-scale power system, is used widely in recent years.
The features such as more, widely distributed for Demand-side InterWorking Equipment type, the present invention propose a kind of Demand-side resource interconnection
Reliability estimation method and system are based on monte carlo method, targetedly select suitable reliability assessment index, to realize
The more accurate reliability assessment of effect is interconnected to Demand-side.
Referring to figs. 1 and 2, a kind of Demand-side proposed by the present invention interconnects reliability estimation method specific steps such as
Under:
The system mode computing system trend that S1 is obtained based on sampling；System mode herein refers specifically under Demand-side interconnection
Electric system.
S2 judges to calculate whether the system load flow obtained meets constraint condition；Continue to calculate if satisfied, then returning to previous step
System load flow, until frequency in sampling reaches preset frequency in sampling；If not satisfied, then being carried out at cutting load to Demand-side
Reason, and with the minimum objective function computing system trend of cutting load amount；
Calculated result of the S3 based on system load flow calculates the Model in Reliability Evaluation of Power Systems index under Demand-side interconnection, base
In the reliability assessment index, the Power System Reliability under Demand-side interconnection is assessed.
Step S1 sampling obtains system mode
A, using non-sequential Monte Carlo method, each equipment shape in simulation system is uniformly distributed on [0,1] by section
State；The normal and malfunction of equipment in simulation system is come in being uniformly distributed for [0,1] using section, and each equipment room is mutual
It is independent, enable siThe state of representation element i, λiRepresent its failure rate, then it is equally distributed between [0,1] to element i generation one
Random number Ri, then have through equipment state each in following formula simulation system:
In formula, siThe state for indicating element i, if si=0, then element i is in running order, if si=1, then element i is in
Malfunction, λiIndicate the failure rate of element i, RiThe equally distributed random number on [0,1] generated for element i.
B, random sampling is carried out to equipment state each in system, forms system mode；The system mode of one N element can be with
It is indicated by vector s:
S=(s1,s2,...,si,...,sN)
In formula, S indicates system mode, and N is that random number extracts number.
C, using sampling frequency as the unbiased esti-mator of system mode probability, the system mode is assessed；
Wherein, each equipment state includes: element operating status, line status and Demand-side resource power output shape in the system
State.When some system mode s is chosen in samplingiAfterwards, then the particular state is analyzed to judge whether it is malfunction, is then
The reliability index of the state can be estimated.When frequency in sampling is enough, certain specific system state vector S is sampling
The frequency occurred in the process can be used as the unbiased esti-mator of its probability:
In formula, M is frequency in sampling, and m (s) is the sampling frequency of system mode s, i.e., system mode S goes out in sampling process
Existing number.
Step S1 includes: that the system mode is defined as ox based on the system mode computing system trend that sampling obtains
- initial value of the inferior method of pressgang, system load flow is calculated using Newton-Raphson approach.
Constraint condition in step S2 includes that there is no out-of-limit for node voltage under current system conditions and branch power.
Based on the calculated result of system load flow in step S3, the Model in Reliability Evaluation of Power Systems calculated under Demand-side interconnection refers to
Mark includes: the alternative capacity that Demand-side resource is determined according to the flow data of node each in system mode and system, failure
Energy loss amount, grid side load peak-valley difference, user side electricity needs and Network congestion risk afterwards.Choose Demand-side substitution of resources
Energy loss amount, grid side load peak-valley difference, user side electricity needs, Network congestion risk indicator, which are used as, after capacity, failure refers to
Mark is assessed, it is contemplated that the complexity of node, and in order to save simulation time, it is taken out using non-sequential Monte Carlo algorithm
Sample.
The concrete meaning and its calculation method of These parameters are as follows:
(1) Demand-side substitution of resources capacity
With gradually popularizing for automatic demand response response, Demand-side resource plays substitution supply side in the power system
The role of capacity.The effect of Demand-side resource in the power system is reasonably played, can be good at the reliability for improving power grid,
But also it can delay or substitute the expansion of part supply side.Following formula is used for the evaluation of Demand-side resource:
SDR(t)=D (t) × β
In formula, D (t) is electricity needs, SDR(t)、Respectively Demand-side resource and Demand-side resource is alternative
Capacity；β and β1Respectively Demand-side resource permeability and demand response substitution of resources coefficient.
(2) energy loss amount after failure
In the reasonable situation of power grid architecture, after failure occurs, the case where for single power consumer energy loss, carry out
After Demand-side interconnection, due to the addition of the equipment such as user side energy storage, the single power consumer electric energy after grid collapses
Loss will reduce.For the energy loss amount of power consumer, we are calculated with following formula:
In formula, X is energy loss amount of the user after grid collapses, t0For the time that failure starts, tfFor failure knot
The time that beam restores electricity, P are that user side is nonserviceabled the electricity demand in the duration.
(3) grid side load peak-valley difference
Reduce the free-revving engine that power grid peak-valley difference is Demand-side interconnection.Peak valley difference value is big, will cause grid stability
Decline.In electricity peak period, user reduces the load for grid side using energy storage device, in low power consumption, uses
The energy storage device and powerful device of family side carry out accumulation of energy and work, to increase the load for grid side, so that power grid
The load of side tends to balance, and reduces power grid peak-valley difference.
Network load peak-valley difference: δ=LMAX-LMIN
In formula: δ is network load peak-valley difference, LMAXAnd LMINRespectively indicate peak load in the malfunction duration with
Minimum load.
(4) user side electricity needs
User side electricity needs receives the influence of two aspects.On the one hand, by the driving of market awareness, power consumer root
According to market guidance signal and the load plan of demand response event and active accommodation itself, this part is by electricity needs price bullet
The form of property coefficient is stated；On the other hand, it is influenced by Macroeconomic Development, the growth of electricity needs and gross national product
The growth of (Gross Domestic Product, GDP) is presented certain associate feature, this part by electricity elasticity coefficients shape
Formula statement:
In formula: δ is network load peak-valley difference, and D (t) indicates user side electricity needs；Δ D (t) indicates that user side electric power needs
Seek increment；T is the time to be predicted；νGDPFor GDP growth rate；ε1、ε2Respectively electricity elasticity coefficients and price elasticity of electricity demand
Coefficient；P1For sales rate of electricity.
(5) Network congestion risk indicator
It in operation of power networks process, is impacted by peak period electric power energy demand, a large amount of electric power energy conveying will
Entire supply network is caused choking phenomenon occur, if such case cannot be discongested effectively, it will lead to large-area power-cuts
Etc. adverse consequences, for social economy produce, residential electricity consumption user life adversely affect.Demand-side interconnection technique can be with
Energy storage device is enabled according to power grid peak Elapsed Time section, to guarantee base of the user when power grid is in peak or the state of emergency
This power demand, and the load of power grid is effectively reduced, certain space is flowed out for excessively crowded electric energy supply line, with this
Guarantee is not in choking phenomenon, significantly reduces the brought economic loss of large-area power-cuts and the generation of event of failure
Rate realizes efficient congestion management in the case where ensuring that social economy's production and living electricity demand obtains and meeting, improves
The security performance of power grid.Network congestion degree is evaluated with following formula:
In formula:For Network congestion degree index；P2For T-D tariff；Cmar,capFor marginal Capacity Cost.
In step S3, it is based on the reliability assessment index, the Power System Reliability under Demand-side interconnection is commented
Estimating includes: energy loss amount, grid side load peak after the alternative capacity for obtaining Demand-side resource according to actual condition, failure
Paddy is poor, one or more index values in user side electricity needs and Network congestion risk be evaluate examining system reliability according to
According to the performance of the electric system under interconnecting to Demand-side to be measured is given a mark, and is supported to provide data for technical staff.Make
Obtaining technical staff targetedly improves Model in Reliability Evaluation of Power Systems method, and interconnects lower electric system for Demand-side
The suffered different degrees of foundation that offer risk control and repair based on condition of component is provided.
Based on the same inventive concept, the application also proposes a kind of Demand-side interconnection reliability evaluation system, comprising:
Decimation blocks, the system mode computing system trend for being obtained based on sampling；
Determining module calculates whether the system load flow obtained meets constraint condition for judging；If satisfied, then returning to upper one
Step continues computing system trend, until the frequency in sampling reaches preset frequency in sampling；If not satisfied, then to Demand-side
Cutting load processing is carried out, and with the minimum target computing system trend of cutting load amount；
Evaluation module calculates the Power System Reliability under Demand-side interconnection for the calculated result based on system load flow
Evaluation index is based on the reliability assessment index, assesses the Power System Reliability under Demand-side interconnection.
Wherein, decimation blocks include:
Simulation submodule is uniformly distributed simulation system on [0,1] by section for using non-sequential Monte Carlo method
Each equipment state in uniting；
Random sampling submodule forms system mode for carrying out random sampling to equipment state each in the system；
Submodule is analyzed, for determining the system mode using sampling frequency as the unbiased esti-mator of system mode probability.
Random sampling submodule includes: generation unit again, for determining system mode by following formula:
S=(s1,s2,...,si,...,sN)
In formula, S indicates system mode, and N is that random number extracts number.
Simulation submodule includes: state simulation unit again, for passing through each equipment state in following formula simulation system:
In formula, siThe state for indicating element i, if si=0, then element i is in running order, if si=1, then element i is in
Malfunction, λiIndicate the failure rate of element i, RiThe equally distributed random number on [0,1] generated for element i.
Analysis submodule comprises determining that unit again, for determining the unbiased esti-mator of system mode probability by following formula:
In formula, M is frequency in sampling, and m (s) is the sampling frequency of system mode s.
Wherein, evaluation module includes: index estimation submodule, for the calculating according to system mode and the system load flow
As a result, determining energy loss amount, grid side load peak-valley difference, user side electric power after the alternative capacity of Demand-side resource, failure
Demand and Network congestion risk.
Index estimation module, including the first computing unit again, for determining replacing for demand response resource by following formula
For capacity:
SDR(t)=D (t) × β
In formula, D (t) is electricity needs, SDR(t)、Respectively Demand-side resource and Demand-side resource is alternative
Capacity；β and β1Respectively Demand-side resource permeability and demand response substitution of resources coefficient；
Second computing unit, for determining the energy loss amount after failure by following formula:
In formula, X is energy loss amount of the user after grid collapses, t0For the time that failure starts, tfFor failure knot
The time that beam restores electricity, P are that user side is nonserviceabled the electricity demand in the duration；
Third computing unit, for determining grid side load peak-valley difference by following formula:
δ=LMAX-LMIN
In formula: δ is network load peak-valley difference, LMAXAnd LMINRespectively indicate peak load in the malfunction duration with
Minimum load；
4th computing unit, for determining user side electricity needs by following formula:
In formula: δ is network load peak-valley difference, and D (t) indicates user side electricity needs；Δ D (t) indicates that user side electric power needs
Seek increment；T is the time to be predicted；νGDPFor GDP growth rate；ε1、ε2Respectively electricity elasticity coefficients and price elasticity of electricity demand
Coefficient；P1For sales rate of electricity；
5th computing unit, for determining Network congestion risk by following formula:
In formula,For Network congestion risk, P2For T-D tariff, Cmar,capFor marginal Capacity Cost.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Claims (21)
1. a kind of Demand-side interconnects reliability estimation method, which is characterized in that the described method includes:
System mode computing system trend based on acquisition；
Judge whether system load flow meets constraint condition；If satisfied, then returning to previous step continues computing system trend, until obtaining
System mode reach preset number；If not satisfied, then carrying out cutting load processing to Demand-side, and most with cutting load amount
Small is target computing system trend；
The system load flow result that system mode based on the acquisition calculates or the system calculated with the minimum target of cutting load amount
Power flow solutions calculate the Model in Reliability Evaluation of Power Systems index under Demand-side interconnection, are based on the reliability assessment index, to need
Power System Reliability under asking side to interconnect is assessed.
2. the method as described in claim 1, which is characterized in that the acquisition system mode includes:
Using non-sequential Monte Carlo method, each equipment state in simulation system is uniformly distributed on [0,1] by section；
Random sampling is carried out to equipment state each in the system, forms system mode；
Using sampling frequency as the unbiased esti-mator of system mode probability, the system mode is determined；
Wherein, each equipment state includes: element operating status, line status and Demand-side resource power output state in the system.
3. method according to claim 2, which is characterized in that determine system mode by following formula:
S=(s1,s2,...,si,...,sN)
In formula, S indicates system mode, and N is that random number extracts number, SNThe system mode randomly selected for n-th.
4. method as claimed in claim 3, which is characterized in that pass through each equipment state in following formula simulation system:
In formula, siThe state for indicating element i, if si=0, then element i is in running order, if si=1, then element i is in failure
State, λiIndicate the failure rate of element i, RiThe equally distributed random number on [0,1] generated for element i.
5. method as claimed in claim 3, which is characterized in that determine the unbiased esti-mator of system mode probability by following formula:
In formula, M is frequency in sampling, and m (s) is the sampling frequency of system mode s.
6. the method as described in claim 1, which is characterized in that the system mode computing system trend packet based on acquisition
It includes: the system mode is defined as to the initial value of Newton-Raphson approach, system load flow is counted using Newton-Raphson approach
It calculates.
7. the method as described in claim 1, which is characterized in that the constraint condition includes the node electricity under current system conditions
There is no out-of-limit for pressure and branch power.
8. the method as described in claim 1, which is characterized in that the Power System Reliability calculated under Demand-side interconnection is commented
Estimating index includes:
The system load flow result that system mode based on the acquisition calculates or the system calculated with the minimum target of cutting load amount
Power flow solutions determine energy loss amount, grid side load peak-valley difference, user side after the alternative capacity of Demand-side resource, failure
Electricity needs and Network congestion risk.
9. method according to claim 8, which is characterized in that determine the alternative capacity of demand response resource by following formula:
In formula,The alternative capacity of demand response resource, β1For demand response substitution of resources coefficient；SDR(t) it is rung for demand
Answer resource.
10. method as claimed in claim 9, which is characterized in that the demand response resource is determined by following formula:
SDR(t)=D (t) × β
In formula, D (t) is electricity needs, and β is demand response resource permeability.
11. method according to claim 8, which is characterized in that the energy loss amount after determining failure by following formula:
In formula, X is energy loss amount of the user after grid collapses, t0For the time that failure starts, tfTerminate for failure extensive
The time powered again, P are that user side is nonserviceabled the electricity demand in the duration.
12. method according to claim 8, which is characterized in that determine grid side load peak-valley difference by following formula:
δ=LMAX-LMIN
In formula: δ is network load peak-valley difference, LMAXAnd LMINRespectively indicate the peak load and minimum in the malfunction duration
Load.
13. method according to claim 8, which is characterized in that determine user side electricity needs by following formula:
In formula: δ is network load peak-valley difference, and D (t) indicates user side electricity needs；Δ D (t) indicates that user side electricity needs increases
Amount；T is the time to be predicted；νGDPFor GDP growth rate；ε1、ε2Respectively electricity elasticity coefficients and Power demand price elasticity coefficient；
P1For sales rate of electricity.
14. method according to claim 8, which is characterized in that determine Network congestion risk by following formula:
In formula,For Network congestion risk, P2For T-D tariff, Cmar,capFor marginal Capacity Cost.
15. a kind of Demand-side interconnects reliability evaluation system, which is characterized in that the system comprises:
Decimation blocks, for the system mode computing system trend based on acquisition；
Determining module, for judging whether the system load flow meets constraint condition；Continue to calculate if satisfied, then returning to previous step
System load flow, until the system mode of acquisition reaches preset number；If not satisfied, then being carried out at cutting load to Demand-side
Reason, and with the minimum target computing system trend of cutting load amount；
Evaluation module, for the system load flow result of the system mode calculating based on the acquisition or with the minimum mesh of cutting load amount
The system load flow calculated is marked as a result, calculating the Model in Reliability Evaluation of Power Systems index under Demand-side interconnection, is based on the reliability
Evaluation index assesses the Power System Reliability under Demand-side interconnection.
16. system as claimed in claim 15, which is characterized in that the decimation blocks include:
Simulation submodule passes through section being uniformly distributed in simulation system on [0,1] for using non-sequential Monte Carlo method
Each equipment state；
Random sampling submodule forms system mode for carrying out random sampling to equipment state each in the system；
Submodule is analyzed, for determining the system mode using sampling frequency as the unbiased esti-mator of system mode probability.
17. system as claimed in claim 16, which is characterized in that the random sampling submodule includes: generation unit, is used for
System mode is determined by following formula:
S=(s1,s2,...,si,...,sN)
In formula, S indicates system mode, and N is that random number extracts number, SNThe system mode randomly selected for n-th.
18. system as claimed in claim 16, which is characterized in that the simulation submodule includes: state simulation unit, is used for
Pass through each equipment state in following formula simulation system:
In formula, siThe state for indicating element i, if si=0, then element i is in running order, if si=1, then element i is in failure
State, λiIndicate the failure rate of element i, RiThe equally distributed random number on [0,1] generated for element i.
19. the method described in claim 16, which is characterized in that the analysis submodule comprises determining that unit, for passing through
Following formula determines the unbiased esti-mator of system mode probability:
In formula, M is frequency in sampling, and m (s) is the sampling frequency of system mode s.
20. system as claimed in claim 19, which is characterized in that the evaluation module includes: index estimation submodule, is used for
System load flow result that system mode based on acquisition calculates or the system load flow calculated with the minimum target of cutting load amount as a result,
Determine energy loss amount after the alternative capacity of Demand-side resource, failure, grid side load peak-valley difference, user side electricity needs and
Network congestion risk.
21. system as claimed in claim 20, which is characterized in that the index estimates submodule, including the first computing unit,
For determining the alternative capacity of demand response resource by following formula:
In formula,The alternative capacity of demand response resource, β1For demand response substitution of resources coefficient；SDR(t) it is rung for demand
Answer resource；
Second computing unit, for determining the energy loss amount after failure by following formula:
In formula, X is energy loss amount of the user after grid collapses, t0For the time that failure starts, tfTerminate for failure extensive
The time powered again, P are that user side is nonserviceabled the electricity demand in the duration；
Third computing unit, for determining grid side load peak-valley difference by following formula:
δ=LMAX-LMIN
In formula: δ is network load peak-valley difference, LMAXAnd LMINRespectively indicate the peak load and minimum in the malfunction duration
Load；
4th computing unit, for determining user side electricity needs by following formula:
In formula: δ is network load peak-valley difference, and D (t) indicates user side electricity needs；Δ D (t) indicates that user side electricity needs increases
Amount；T is the time to be predicted；νGDPFor GDP growth rate；ε1、ε2Respectively electricity elasticity coefficients and Power demand price elasticity coefficient；
P1For sales rate of electricity；
5th computing unit, for determining Network congestion risk by following formula:
In formula,For Network congestion risk, P2For T-D tariff, Cmar,capFor marginal Capacity Cost.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110097292A (en) * | 2019-05-14 | 2019-08-06 | 华北电力大学 | A kind of Demand-side interconnection effect reliability estimation method based on Monte Carlo |
| CN111353230A (en) * | 2020-03-03 | 2020-06-30 | 中国海洋石油集团有限公司 | Reliability assessment method and system for offshore oilfield power system |
| CN111563763A (en) * | 2020-04-16 | 2020-08-21 | 广东卓维网络有限公司 | Power load demand response management system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069701A (en) * | 2015-08-10 | 2015-11-18 | 国网上海市电力公司 | Monte Carlo method based risk evaluation method for power transmission system |
| CN106096827A (en) * | 2016-06-02 | 2016-11-09 | 国网山东省电力公司济南供电公司 | The reliability calculation method calculated with minimum tangential load based on mixed sampling and platform |
| CN106228459A (en) * | 2016-04-21 | 2016-12-14 | 重庆大学 | Equivalent reliability estimation method based on Monte Carlo |
| CN106485392A (en) * | 2016-07-29 | 2017-03-08 | 国家电网公司 | Reliability of Interconnected Generating System appraisal procedure |
-
2018
- 2018-08-06 CN CN201810883260.6A patent/CN109149559A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069701A (en) * | 2015-08-10 | 2015-11-18 | 国网上海市电力公司 | Monte Carlo method based risk evaluation method for power transmission system |
| CN106228459A (en) * | 2016-04-21 | 2016-12-14 | 重庆大学 | Equivalent reliability estimation method based on Monte Carlo |
| CN106096827A (en) * | 2016-06-02 | 2016-11-09 | 国网山东省电力公司济南供电公司 | The reliability calculation method calculated with minimum tangential load based on mixed sampling and platform |
| CN106485392A (en) * | 2016-07-29 | 2017-03-08 | 国家电网公司 | Reliability of Interconnected Generating System appraisal procedure |
Non-Patent Citations (1)
| Title |
|---|
| PENGCHENG XU 等: "A new approach for fast reliability evaluation of omposite power system considering wind farm", 《PREPRINTS OF THE 5TH INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110097292A (en) * | 2019-05-14 | 2019-08-06 | 华北电力大学 | A kind of Demand-side interconnection effect reliability estimation method based on Monte Carlo |
| CN111353230A (en) * | 2020-03-03 | 2020-06-30 | 中国海洋石油集团有限公司 | Reliability assessment method and system for offshore oilfield power system |
| CN111563763A (en) * | 2020-04-16 | 2020-08-21 | 广东卓维网络有限公司 | Power load demand response management system |
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