CN111832936B - Distribution network power supply reliability assessment method containing distributed power supply - Google Patents
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Abstract
The invention belongs to the technical field of power grid operation safety, and particularly relates to a distribution network power supply reliability assessment method containing a distributed power supply. The method comprises the following steps: collecting data of historical wind speed of an area where the power distribution network is located; setting data of wind speed of a fan; calculating fan output according to the wind speed data to form a fan output sequence; collecting historical load data of a power distribution network; setting a normal distribution function of the load, and randomly generating a load sequence from the distribution function; calculating a storage battery output sequence according to the fan output sequence and the load sequence; and calculating the reliability index of the sampling period according to the fan output sequence, the load sequence and the storage battery output sequence. The invention describes the operation characteristics of the distribution network based on the historical data and the random data, improves the accuracy of the power supply reliability, is favorable for reasonably quantifying the reliability benefit of the distributed power supply access, and is suitable for the great popularization and application of the power grid operation industry.
Description
Technical Field
The invention belongs to the technical field of power grid operation safety, and particularly relates to a distribution network power supply reliability assessment method containing a distributed power supply.
Background
In consideration of environmental benefits and sustainable development of energy, the distributed power supply is rapidly developed, the tide supply relation of the power distribution network is changed, the reliability of the power distribution network is affected, and economic benefits are improved. Meanwhile, the distributed power supply access increases the cost, and when a planning department formulates a distributed power supply access scheme, the reliability improvement benefit and the access cost of the distributed power supply access network are comprehensively considered, and the reasonable distributed power supply capacity is determined, so that the reliability evaluation of the power supply network containing the distributed power supply is required to be accurately evaluated.
The prior distributed power supply reliability assessment mainly adopts a Monte Carlo method, adopts a random simulation method to sample the output of the distributed power supply, and has the defects of long time consumption and incapability of truly reflecting the processing characteristics of specific areas.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a power supply reliability evaluation method for a distribution network containing a distributed power supply. The method aims to describe the operation characteristics of the distribution network based on historical data and random data, improve the accuracy of power supply reliability and help to reasonably quantify the reliability benefit of distributed power supply access.
The technical scheme adopted by the invention for achieving the purpose is as follows:
a power supply reliability evaluation method for a distribution network with a distributed power supply comprises the following steps:
step 1, collecting historical wind speed data of a region where a power distribution network is located;
step 2, setting data of wind speed of a fan;
step 3, calculating fan output according to the wind speed data to form a fan output sequence;
step 4, collecting historical load data of the power distribution network;
step 5, setting a normal distribution function of the load, and randomly generating a load sequence from the distribution function;
step 6, calculating a storage battery output sequence according to the fan output sequence and the load sequence;
and 7, calculating the reliability index of the sampling period according to the fan processing sequence, the load sequence and the storage battery output sequence.
The method comprises the steps of collecting historical wind speed data of a region where a power distribution network is located, wherein a sampling interval is deltat, a sampling time length is T, a wind speed data sequence H [ v (T) … v (t+ndeltat) … v (T) ] is formed, H represents that the data sequence consists of historical data, and v represents wind speed.
The data for setting the wind speed of the fan is a Weibull distribution function f (v) for setting the wind speed of the fan, a wind speed sequence S [ v (T) … v (t+nDeltat) … v (T) ] is randomly generated according to the distribution function, S represents that the data sequence consists of random data, and n represents the sampling number.
The fan output is calculated according to the wind speed data to form a fan output sequence:
H[P w (t)…P w (t+nΔt)…P w (T)]and S [ P ] w (t)…P w (t+nΔt)…P w (T)]The calculation formula is as follows:
wherein: h means that the data sequence consists of historical data, S means that the data sequence consists of random data, P w (t) wind power output power at t moment; p (P) s Is the rated power of the fan; v ci Is the cut-in wind speed; v r Is the rated wind speed; v o Cutting off the wind speed; a, B, C are constants.
The collecting historical load data of the power distribution network comprises the following steps: the sampling interval is deltat, the sampling duration is T, and a load data sequence H [ L (T) … L (t+ndeltat) … L (T) ] is formed, wherein L (T) is the load demand at the moment T.
The method comprises the steps of setting a normal distribution function of a load, and randomly generating a load sequence from the distribution function, wherein the normal distribution function f (v) of the load is set, and the random distribution function randomly generates the load sequence:
S[L(t)…L(t+nΔt)…L(T)]
where σ represents the standard deviation, m represents the average value, and L represents the load.
The storage battery output sequence is calculated according to the fan output sequence and the load sequence:
S[P b (t)…P b (t+nΔt)…P b (T)]and H [ P ] b (t)…P b (t+nΔt)…P b (T)]
P in the formula b And (t) is the output of the storage battery at the moment t.
The method for calculating the output sequence of the storage battery according to the output sequence and the load sequence of the fan comprises the following steps:
step (1) calculating a storage battery output data sequence:
P b (t)≤E b (t+Δt)-E b (t)
p in the formula b (t) the output of the storage battery at the moment t, wherein positive numbers represent charging and negative numbers represent discharging; p (P) dm Representing the maximum discharge power; p (P) cm Represents maximum charging power, P w Represents wind power output power E b Representing the storage energy of the storage battery;
step (2) a stored energy data sequence of the storage battery:
in E b (t) is the energy stored in the storage battery at the moment t, E b (t+Δt) is the storage energy of the storage battery at the time t+Δt; e (E) max Representing maximum capacity; e (E) min Representing minimum capacity, P b And (t) is the output of the storage battery at the moment t.
The reliability index of the sampling period is calculated according to the fan processing sequence, the load sequence and the storage battery output sequence:
wherein H (LOL) and S (LOL) respectively represent the power distribution network load loss reliability index based on historical data and random data, LOL represents the power distribution network load loss reliability index, and P b (t) is the output of the storage battery at the moment t, P w (t) represents the wind power output power at the time t, and pr represents a probability function.
A computer storage medium having a computer program stored thereon, the computer program when executed by a processor implementing the steps of a method for evaluating power supply reliability of a distribution network comprising a distributed power source.
The invention has the following beneficial effects and advantages:
the invention describes the operation characteristics of the distribution network based on the historical data and the random data, improves the accuracy of the power supply reliability, is favorable for reasonably quantifying the reliability benefit of the distributed power supply access, and is suitable for the great popularization and application of the power grid operation industry.
The invention considers the energy storage element and can accurately analyze the charge and discharge conditions of the energy storage and the reliable power supply influence of the power distribution network.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
The following describes some embodiments of the present invention with reference to fig. 1.
Example 1
The invention relates to a distribution network power supply reliability assessment method containing a distributed power supply, which is shown in figure 1 and comprises the following steps:
step 1, collecting historical wind speed data of a region where a power distribution network is located;
step 2, setting data of wind speed of a fan;
step 3, calculating fan output according to the wind speed data to form a fan output sequence;
step 4, collecting historical load data of the power distribution network;
step 5, setting a normal distribution function of the load, and randomly generating a load sequence from the distribution function;
step 6, calculating a storage battery output sequence according to the fan output sequence and the load sequence;
step 6.1, calculating a storage battery output data sequence;
step 6.2, calculating a stored energy data sequence of the storage battery;
and 7, calculating the reliability index of the sampling period according to the fan output sequence, the load sequence and the storage battery output sequence. I.e. the loss of load reliability index of the distribution network.
Example 2
The invention relates to a distribution network power supply reliability assessment method containing a distributed power supply, which is shown in figure 1 and comprises the following steps:
step 1, collecting data of historical wind speed of a region where the power distribution network is located, wherein a sampling interval is deltat, a sampling time length is T, a wind speed data sequence H [ v (T) … v (t+ndeltat) … v (T) ] is formed, H represents the data sequence to be composed of historical data, and v represents wind speed.
Step 2, setting a Weibull distribution function f (v) of the wind speed of the fan, randomly generating a wind speed sequence S [ v (T) … v (t+nDeltat) … v (T) ] according to the distribution function, wherein S represents that the data sequence consists of random data, and n represents the sampling number.
Step 3, calculating fan output according to the wind speed data to form a fan output sequence HP w (t)…P w (t+nΔt)…P w (T)]And S [ P ] w (t)…P w (t+nΔt)…P w (T)]The calculation formula is as follows:
wherein: h means that the data sequence consists of historical data, S means that the data sequence consists of random data, P w (t) wind power output power at t moment; p (P) s Is the rated power of the fan;v ci is the cut-in wind speed; v r Is the rated wind speed; v o Cutting off the wind speed; a, B, C are constants
Step 4, collecting historical load data of the power distribution network, wherein the sampling interval is deltat, the sampling time length is T, and a load data sequence H [ L (T) … L (t+ndeltat) … L (T) ] is formed, wherein L (T) is the load demand at the moment T
Step 5, setting a normal distribution function f (v) of the load, and randomly generating a load sequence by using a random distribution function
S[L(t)…L(t+nΔt)…L(T)]
Where σ represents the standard deviation, m represents the average value, and L represents the load.
Step 6, calculating the output sequence SP of the storage battery according to the output sequence and the load sequence of the fan b (t)…P b (t+nΔt)…P b (T)]And H [ P ] b (t)…P b (t+nΔt)…P b (T)]
P in the formula b And (t) is the output of the storage battery at the moment t.
Step 6.1 calculating the output data sequence of the storage battery
P b (t)≤E b (t+Δt)-E b (t)
P in the formula b (t) is the output of the storage battery at the moment t, positive number represents charging, negative number represents discharging, P dm Represents maximum discharge power, P cm Represents the maximum charging power, P w Represents wind power output power E b Representing the storage energy of the accumulator.
Step 6.2 stored energy data sequence of the Battery
In E b (t) is the energy stored in the storage battery at the moment t, E b (t+Δt) is the storage energy of the storage battery at the time t+Δt; e (E) max Representing maximum capacity; e (E) min Representing minimum capacity, P b (t) is the output of the storage battery at the moment t, E b And (t) represents the storage energy of the storage battery at the time t.
And 7, calculating the reliability index of the sampling period according to the fan output sequence, the load sequence and the storage battery output sequence.
Wherein H (LOL) and S (LOL) respectively represent the power distribution network load loss reliability index based on historical data and random data, LOL represents the power distribution network load loss reliability index, and P b (t) is the output of the storage battery at the moment t, P w (t) represents the wind power output power at the time t, and pr represents a probability function.
Example 3
Based on the same inventive concept, the embodiment of the present invention further provides a computer storage medium, where a computer program is stored, where the computer program when executed by a processor implements the steps of the method for evaluating power supply reliability of a distribution network including a distributed power supply described in embodiment 1 or 2.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (2)
1. A distribution network power supply reliability assessment method containing a distributed power supply is characterized by comprising the following steps: the method comprises the following steps: step 1, collecting historical wind speed data of a region where a power distribution network is located; the sampling interval is deltat, the sampling time length is T, a wind speed data sequence H [ v (T) … v (t+ndeltat) … v (T) ] is formed, H represents that the data sequence consists of historical data, and v represents wind speed; step 2, setting data of wind speed of a fan; setting a Weibull distribution function f (v) of the wind speed of a fan, randomly generating a wind speed sequence S [ v (T) … v (t+nDeltat) … v (T) ] according to the distribution function, wherein S represents that the wind speed sequence consists of random data, and n represents the sampling number; step 3, calculating fan output according to the wind speed data to form a fan output sequence; comprising the following steps:
H[P w (t)…P w (t+nΔt)…P w (T)]and S [ P ] w (t)…P w (t+nΔt)…P w (T)]The calculation formula is as follows:
wherein: h represents that the wind speed sequence consists of historical data, S represents that the wind speed sequence consists of random data and P represents that the wind speed sequence consists of random data w (t) wind power output power at t moment; p (P) s Is the rated power of the fan; v ci Is the cut-in wind speed; v r Is the rated wind speed; v o Cutting off the wind speed; a, B, C are constants; step 4, collecting historical load data of the power distribution network; comprising the following steps: the sampling interval is Deltat, the sampling duration is T, and a load data sequence H [ L (T) … L (t+nDeltat) … L (T) is formed]Wherein L (t) is the load demand at time t; step 5, setting a normal distribution function of the load, and randomly generating a load sequence from the distribution function; comprising the following steps: setting a normal distribution function f (v) of the load, and randomly generating a load sequence by a random distribution function:
S[L(t)…L(t+nΔt)…L(T)]
wherein sigma represents a standard deviation, m represents an average value, and L represents a load; step 6, calculating a storage battery output sequence according to the fan output sequence and the load sequence; the fan output sequence and the load sequence comprise: s [ P ] b (t)…P b (t+nΔt)…P b (T)]And H [ P ] b (t)…P b (t+nΔt)…P b (T)]P in the formula b (t) battery output at time t; the method for calculating the output sequence of the storage battery according to the output sequence and the load sequence of the fan comprises the following steps: step (1) calculating a storage battery output data sequence:
P b (t)≤E b (t+Δt)-E b (t)
p in the formula b (t) the output of the storage battery at the moment t, wherein positive numbers represent charging and negative numbers represent discharging; p (P) dm Representing the maximum discharge power; p (P) cm Represents maximum charging power, P w Represents wind power output power E b Representing the storage energy of the storage battery; step (2) a stored energy data sequence of the storage battery:
in E b (t) is the energy stored in the storage battery at the moment t, E b (t+Δt) is the storage energy of the storage battery at the time t+Δt; e (E) max Representing maximum capacity; e (E) min Representing minimum capacity, P b (t) battery output at time t; step 7, calculating reliability indexes of a sampling period according to the fan processing sequence, the load sequence and the storage battery output sequence; the following are provided:
wherein H (LOL) and S (LOL) respectively represent the power distribution network load loss reliability index based on historical data and random data, LOL represents the power distribution network load loss reliability index, and P b (t) is the output of the storage battery at the moment t, P w (t) represents the wind power output power at the time t, and pr represents a probability function.
2. A computer storage medium, characterized by: the computer storage medium has a computer program stored thereon, which when executed by a processor implements the steps of a method for evaluating power supply reliability of a distribution network comprising a distributed power supply according to claim 1.
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