CN117411080A - Configuration method of flexible rigidity regulating device for distributed clean energy - Google Patents
Configuration method of flexible rigidity regulating device for distributed clean energy Download PDFInfo
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Classifications
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00016—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using a wired telecommunication network or a data transmission bus
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
Abstract
The invention relates to the technical field of distributed clean energy regulation and control, in particular to a configuration method of a distributed clean energy flexible rigid regulation and control device. Comprising the following steps: constructing a reverse overload model of the transformer in the distribution area; providing a distribution transformer reverse overload grade evaluation model; the configuration strategy of the distributed clean energy flexible rigid regulating device suitable for different distributed clean energy output characteristics and adjustable characteristics and the reverse overload grade of the distribution transformer of the transformer area is provided. The design of the invention considers the different output characteristics and the adjustable characteristics of the distributed clean energy and the configuration strategy of the distributed clean energy flexible rigid adjusting device of the reverse overload grade of the distribution transformer of the transformer area, has wide applicability and is suitable for the high-proportion distributed transformer area scene; the configuration strategy of the distributed clean energy flexible rigid regulation device with replicability and generalizability is provided through analysis, so that the application of the device for nationwide mass high-proportion distributed transformer area scenes is guided.
Description
Technical Field
The invention relates to the technical field of distributed clean energy regulation and control, in particular to a configuration method of a distributed clean energy flexible rigid regulation and control device.
Background
With the sustainable development of distributed clean energy and grid connection, the safe operation of a power distribution network faces outstanding problems and challenges, and a great number of transformer burnout events caused by reverse overload of the generated power of the clean energy already occur in some provinces in China, so that the power supply reliability and the power supply safety are seriously affected.
Aiming at the safety operation problem brought by the access of the distributed clean energy into the power distribution network, a plurality of researches and application practices are developed at present, and in the aspect of safety operation control, the technical measures mainly comprise: firstly, a fusion terminal is additionally arranged at the side of a platform area, and a regulating and controlling device such as a clean energy partner is additionally arranged at the side of a clean energy inverter, and an active regulating instruction is issued to the clean energy inverter through the regulating and controlling device such as the clean energy partner when the reverse overload of the platform area is serious; secondly, an intelligent breaker is additionally arranged on the side of the clean energy grid-connected point, and the intelligent breaker can be directly disconnected when the reverse overload of the transformer area is serious. The two technical measures have the application scene and the advantages that the two technical measures are applicable to the clean energy inverter which is provided with a communication interface and supports remote active regulation, and the clean energy inverter has the characteristics of flexible regulation and low installation cost; the latter is suitable for clean energy inverter without communication interface or remote active regulation, although only realizing off-grid rigid control, not flexible regulation, and the installation cost is higher than that of clean energy mate, but the controllability of distributed clean energy is also solved, which is helpful to ensure safe operation of the platform area.
However, the two technical measures have problems in configuration at present, in the application practice which is developed at present, most of the distributed clean energy under the platform area is fully covered in an adjustable or controllable manner, namely, a clean energy partner or an intelligent breaker is fully arranged on the side of the distributed clean energy under the platform area, which definitely brings huge investment problems, and meanwhile, the operation and maintenance problems of mass equipment are also faced.
Therefore, how to implement on-demand configuration of a distributed clean energy flexible rigid regulation device ("clean energy companion" and clean energy intelligent circuit breaker are collectively referred to as a distributed clean energy flexible rigid regulation device) according to the safe operating requirements of a distribution transformer of a transformer area is a challenge. In view of the above, we propose a method for configuring a distributed clean energy flexible rigid regulation device.
Disclosure of Invention
The invention aims to provide a configuration method of a distributed clean energy flexible rigidity regulating device, which aims to solve the problems in the background technology.
In order to solve the above technical problems, one of the purposes of the present invention is to provide a method for configuring a distributed clean energy flexible rigid control device, comprising the following steps:
s1, constructing a reverse overload model of a transformer in a power distribution station, which specifically comprises the following steps:
s1.1, constructing a platform region distributed clean energy time sequence dynamic data model: taking account of the fact that electric quantity data of all distributed clean energy sources under a station area are collected, firstly, providing a station area distributed clean energy source cluster dividing method based on the electric quantity data, providing a cluster power estimating method based on active monitoring/prediction of a marker post unit, and further establishing a station area distributed clean energy source time sequence power characteristic model;
s1.2, constructing a platform region full load time sequence dynamic data model: taking account of the fact that the distribution transformer of the platform area has realized active and reactive net load monitoring, according to the net load monitoring data of the distribution transformer of the platform area, combining the active and reactive data of the clean energy of the platform area, firstly constructing a platform area full load calculation model, and further constructing a platform area full load time sequence dynamic data model based on the platform area distributed clean energy time sequence power data and the platform area full load calculation result;
s1.3, constructing a reverse overload model of a distribution transformer of a transformer area;
s2, providing a distribution transformer reverse overload grade evaluation model, which specifically comprises the following steps:
s2.1, a distribution transformer burnout probability model;
s2.2, a reverse overload grade evaluation model of the distribution transformer;
s3, providing a configuration strategy of the distributed clean energy flexible rigid regulation device adapting to different distributed clean energy output characteristics and adjustable characteristics and the reverse overload level of the distribution transformer of the transformer area, wherein the configuration strategy specifically comprises the following steps:
s3.1, analyzing the output characteristics and the adjustable characteristics of the distributed clean energy of the area;
s3.2, configuring a strategy of the flexible rigid regulating device of the distributed clean energy.
As a further improvement of the present technical solution, in the step S1.1, when the platform area distributed clean energy time sequence dynamic data model is constructed, the platform area distributed clean energy cluster dividing method based on the electric quantity data includes:
let s i (T) is the unit installed active power quantity (i.e. the active power quantity generated by the clean energy divided by the installed capacity) of the T th measurement interval of the distributed clean energy i, and the unit installed active power quantity data set of the clean energy generated in the T continuous measurement intervals is:
s i ＝[s i (1)，s i (2)，...，s i (T)]
the unit installed active power quantity of the nth metering interval of the N distributed clean energy sources under the platform area is recorded as S (t), and the unit installed active power quantity is expressed as follows:
S(t)＝[s 1 (t)，s 2 (t)，...，s N (t)]
the data set of the unit installed active power quantity of N distributed clean energy sources in T continuous metering intervals under the platform area is as follows:
S＝[s 1 ，s 2 ，...，s N ]
adopting a K-means clustering method, and dividing S into K clusters on the basis of the principle that the L2 norm distance (such as Euclidean distance) is minimum;
the L2 norm distance between the unit installed active power quantity data sets of the distributed clean energy sources a and b is as follows:
d ab ＝||s a -s b ||
wherein s is a 、s b And the active electricity quantity data sets are respectively installed for the distributed clean energy source a and the unit of the distributed clean energy source a in T continuous metering intervals.
As a further improvement of the technical scheme, in the step S1.1, when the platform area distributed clean energy time sequence dynamic data model is constructed, the cluster power estimation method based on active monitoring/prediction of the marker post unit comprises the following steps:
according to the K distributed clean energy clusters after clustering, a marker post unit is taken from each cluster K, and the similarity of the power generation capacity of the clean energy in one cluster is considered, so that a cluster power estimation model is as follows:
wherein P is k (t) is the active estimated value of the distributed clean energy cluster k at the time t,total capacity of the distributed clean energy cluster k, +.>Active monitoring/predicting value of marker post unit of distributed clean energy cluster k at time t, < >>The installed capacity of the marker post unit of the distributed clean energy cluster k; q (Q) k (t) is the reactive power estimated value of the distributed clean energy cluster k at the moment t, < ->Total capacity of inverter for distributed clean energy cluster k, +.>Reactive monitoring/planning value of the pole set at time t for distributed clean energy cluster k, +.>The inverter capacity of the marker post unit of the distributed clean energy cluster k;
furthermore, the method establishes a platform region distributed clean energy time sequence power data model as follows:
based on the power estimated value of the distributed clean energy cluster k at the time t, the time sequence active and reactive data of all the distributed clean energy under the platform area can be obtained, namely:
wherein P is g (t)、Q g And (t) the time sequence active and reactive data of all the distributed clean energy sources in the platform area at the time t respectively.
As a further improvement of the present technical solution, in the step S1.2, when the platform area full load time sequence dynamic data model is constructed, the constructed platform area full load calculation model is:
P d (t)＝P tr (t)+P g (t)
Q d (t)＝Q tr (t)+Q g (t)
wherein P is tr (t)、Q tr (t) active and reactive monitoring of the distribution transformer in the transformer area at the moment t respectivelyMeasuring, i.e. the active, reactive net load, P of the area d (t)、Q d And (t) respectively obtaining the full active load and the full reactive load of the platform region.
As a further improvement of the present technical solution, in the step S1.2, the construction of the platform area full load time sequence dynamic data model specifically includes:
for active time sequence dynamic data of the total load of the platform area at the future moment, training and predicting by adopting a neural network model according to the active historical result of the total load of the platform area obtained by calculation and combining the distributed clean energy time sequence power data model of the platform area;
the input data trained by the neural network model are the real data of the time sequence of the clean energy source of the area region, the output data are the real data of the time sequence of the total load of the area region, and the trained neural network model is adopted to predict the real data of the time sequence of the total load of the area region, namely:
P d (t)＝net P (P g (t))
wherein net is P (. Cndot.) is the trained neural network model;
for reactive time sequence dynamic data of the total load of the transformer area at the future moment, according to the active data, the active and reactive historical calculation results of the total load of the transformer area are combined to estimate:
wherein Γ is the active power of P in the historical calculation result of the total load of the platform region d Data points of (t), Q d (τ) is the reactive data for the τ data point.
As a further improvement of the technical scheme, in the step 1.3, according to the above-mentioned platform region distributed clean energy time sequence dynamic data model and platform region full load time sequence dynamic data model, a reverse overload model of the platform region distribution transformer is constructed as follows:
P tr (t)＝P g (t)-P d (t)
Q tr (t)＝Q g (t)-Q d (t)
wherein S is tr For the rated capacity of the distribution transformer of the transformer area, epsilon (·) is a step function, and lambda- (t) is the reverse overload rate of the distribution transformer of the transformer area at the moment t.
As a further improvement of the technical scheme, in the step S2.1, when the power distribution transformer burnout probability model is constructed, the probability of the power distribution transformer burnout is related to the load rate and the duration time, and the larger the load rate is, the longer the duration time is, the larger the burnout probability is, and a double-exponential model of the reverse load rate and the time integral thereof is adopted, namely:
wherein, xi (t) is the burning probability of the distribution transformer, e (·) As a function of the index of the values,is the maximum allowable value of reverse overload;
h (t) is the integral of the reverse overload rate of the distribution transformer over the duration, namely:
wherein t is 0 To reverse overload start time, h max Is the allowed maximum value of h (t).
As a further improvement of the technical scheme, in the step S2.2, when a reverse overload grade evaluation model of the distribution transformer is constructed, the reverse overload grade of the distribution transformer is rated according to the burnout probability model of the distribution transformer, and the evaluation model is as follows:
wherein r (t) is the reverse overload grade of the distribution transformer, and is sequentially divided into A according to the burning probability value of the distribution transformer + Seven grades, A, B, C, D, E, F, the higher the probability of a distribution transformer burn out, the higher the reverse overload grade.
As a further improvement of the technical scheme, in the step S3.1, when analyzing the output characteristics and the adjustable characteristics of the district-distributed clean energy, according to the above-mentioned district-distributed clean energy cluster division and the active estimation result thereof, and in combination with whether each distributed clean energy inverter has a communication interface and an adjustment function, or whether the intelligent circuit breaker is configured at the grid-connected point of the clean energy power generation, the output characteristics and the adjustable characteristics of the district-distributed clean energy are analyzed as follows:
(1) Acquiring the partition and the active estimation result of the distributed clean energy clusters in the platform area;
(2) Aiming at each cluster, classifying the distributed clean energy inverters with communication interfaces and adjusting functions in the cluster into a group, namely an adjustable group;
(3) Aiming at each cluster, the distributed clean energy sources with the condition of installing the intelligent circuit breaker at the grid-connected point in the cluster are classified as a controllable unit.
As a further improvement of the technical scheme, in step S3.2, different output characteristics and adjustable characteristics of the distributed clean energy and reverse overload levels of the distribution transformer in the transformer area are considered, and a differential configuration strategy of the flexible rigid adjustment device of the distributed clean energy is provided, which is specifically as follows:
(1) According to the method for clustering the distributed clean energy sources in the transformer areas, in order to ensure the reference function of the marker post units in each cluster, the marker post units in each cluster are not used as adjustable units (comprising the adjustable units and the controllable units), namely the marker post units do not need to be provided with flexible or rigid adjusting and controlling devices;
(2) Reverse overload class A for distribution transformer + Regions of A, B, C, D, E, adjustable units in each distributed clean energy cluster under the regions are according to adjustable energyThe power is from big to small, and a 'clean energy companion' flexible regulating device is sequentially configured until the reverse overload level of the distribution transformer is reduced to the F level; if the adjustable capacity of the adjustable units in each distributed clean energy cluster in the platform area is insufficient to enable the reverse overload level of the distribution transformer to be reduced to the F level, the controllable units in each distributed clean energy cluster in the platform area are sequentially provided with intelligent grid-connected point circuit breakers according to the controllable capacity from large to small until the reverse overload level of the distribution transformer is reduced to the F level;
(3) For a distribution transformer reverse overload grade F transformer area, each distributed clean energy source under the transformer area does not need to be provided with a flexible rigidity regulating device.
The second object of the present invention is to provide a platform for a distributed clean energy flexible rigid control device, which includes a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the processor is configured to implement the steps of any one of the above-mentioned distributed clean energy flexible rigid control device configuration methods when executing the computer program.
It is a further object of the present invention to provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of any of the above-described distributed clean energy flexible rigidity control arrangement configuration methods.
Compared with the prior art, the invention has the beneficial effects that:
1. the configuration method of the distributed clean energy flexible rigidity regulating device comprises a platform region distributed clean energy time sequence dynamic data model and a platform region full load time sequence dynamic data model, wherein the platform region distributed clean energy time sequence dynamic data model can comprehensively reflect the reverse overload condition of a platform region distribution transformer;
2. in the configuration method of the distributed clean energy flexible rigid regulating device, a distribution transformer burnout probability model and a distribution transformer reverse overload grade evaluation model are constructed, and a distributed clean energy flexible rigid regulating device configuration strategy which considers different output characteristics and adjustable characteristics of the distributed clean energy and the reverse overload grade of the distribution transformer of the platform area is provided, so that the method has wide applicability and is suitable for a large-scale distributed platform area scene in China;
3. in the configuration method of the distributed clean energy flexible rigid regulating device, a distribution transformer reverse overload grade evaluation model is constructed by analyzing the dynamic matching characteristics of the source-load time sequence of the transformer area, so that a configuration strategy of the distributed clean energy flexible rigid regulating device with replicability and generalizability is provided to guide popularization and application of the distributed transformer area scene with high mass proportion in the whole country.
Drawings
FIG. 1 is a schematic flow diagram of an exemplary method of the present invention;
fig. 2 is a block diagram of an exemplary electronic computer platform according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, one of the purposes of this embodiment is to provide a method for configuring a distributed clean energy flexible rigidity control device, which includes the following steps:
s1, constructing a reverse overload model of a transformer in a power distribution station, which specifically comprises the following steps:
s1.1, constructing a platform region distributed clean energy time sequence dynamic data model: considering that the electric quantity data of each distributed clean energy under the station area are collected, the scheme provides a station area distributed clean energy cluster dividing method based on the electric quantity data, and provides a cluster power estimating method based on active monitoring/prediction of a marker post unit, so that a station area distributed clean energy time sequence power characteristic model is established;
A. the method for dividing the power data-based station area distributed clean energy clusters comprises the following steps:
let s i (T) is the unit installed active power quantity (i.e. the active power quantity generated by the clean energy divided by the installed capacity) of the T th measurement interval of the distributed clean energy i, and the unit installed active power quantity data set of the clean energy generated in the T continuous measurement intervals is:
s i ＝[s i (1)，s i (2)，...，s i (T)]
the unit installed active power quantity of the nth metering interval of the N distributed clean energy sources under the platform area is recorded as S (t), and the unit installed active power quantity is expressed as follows:
S(t)＝[s 1 (t)，s 2 (t)，...，s N (t)]
the data set of the unit installed active power quantity of N distributed clean energy sources in T continuous metering intervals under the platform area is as follows:
S＝[s 1 ，s 2 ，...，s N ]
adopting a K-means clustering method, and dividing S into K clusters on the basis of the principle that the L2 norm distance (such as Euclidean distance) is minimum;
the L2 norm distance between the unit installed active power quantity data sets of the distributed clean energy sources a and b is as follows:
d ab ＝||s a -s b ||
wherein s is a 、s b And the active electricity quantity data sets are respectively installed for the distributed clean energy source a and the unit of the distributed clean energy source a in T continuous metering intervals.
B. The cluster power estimation method based on the active monitoring/prediction of the marker post unit comprises the following steps:
according to the K distributed clean energy clusters after clustering, a marker post unit is taken from each cluster K, and the similarity of the power generation capacity of the clean energy in one cluster is considered, so that a cluster power estimation model is as follows:
wherein P is k (t) is the active estimated value of the distributed clean energy cluster k at the time t,total capacity of the distributed clean energy cluster k, +.>Active monitoring/predicting value of marker post unit of distributed clean energy cluster k at time t, < >>The installed capacity of the marker post unit of the distributed clean energy cluster k; q (Q) k (t) is the reactive power estimated value of the distributed clean energy cluster k at the moment t, < ->Total capacity of inverter for distributed clean energy cluster k, +.>Reactive monitoring/planning value of the pole set at time t for distributed clean energy cluster k, +.>Inverter capacity of the pole set of distributed clean energy cluster k.
C. The method for establishing the platform region distributed clean energy time sequence power data model comprises the following steps:
based on the power estimated value of the distributed clean energy cluster k at the time t, the time sequence active and reactive data of all the distributed clean energy under the platform area can be obtained, namely:
wherein P is g (t)、Q g And (t) the time sequence active and reactive data of all the distributed clean energy sources in the platform area at the time t respectively.
S1.2, constructing a platform region full load time sequence dynamic data model: according to the scheme, according to the net load monitoring data of the distribution transformer of the transformer area, the active and reactive data of clean energy of the distribution transformer area are combined, a total load calculation model of the transformer area is firstly constructed, and then a total load time sequence dynamic data model of the transformer area is constructed based on the time sequence power data of the distributed clean energy of the transformer area and the total load calculation result of the transformer area;
A. the constructed platform area full load calculation model is as follows:
P d (t)＝P tr (t)+P g (t)
Q d (t)＝Q tr (t)+Q g (t)
wherein P is tr (t)、Q tr (t) are respectively the active and reactive monitoring values of the distribution transformer of the transformer area at the moment t, namely the active and reactive net loads of the transformer area, P d (t)、Q d And (t) respectively obtaining the full active load and the full reactive load of the platform region.
B. The construction of the platform region full load time sequence dynamic data model specifically comprises the following steps:
for active time sequence dynamic data of the total load of the platform area at the future moment, training and predicting by adopting a neural network model according to the active historical result of the total load of the platform area obtained by calculation and combining the distributed clean energy time sequence power data model of the platform area;
the input data trained by the neural network model are the real data of the time sequence of the clean energy source of the area region, the output data are the real data of the time sequence of the total load of the area region, and the trained neural network model is adopted to predict the real data of the time sequence of the total load of the area region, namely:
P d (t)＝net P (P g (t))
wherein net is P (. Cndot.) is trained neural netA complex model;
for reactive time sequence dynamic data of the total load of the transformer area at the future moment, according to the active data, the active and reactive historical calculation results of the total load of the transformer area are combined to estimate:
wherein Γ is the active power of P in the historical calculation result of the total load of the platform region d Data points of (t), Q d (τ) is the reactive data for the τ data point.
S1.3, constructing a reverse overload model of a distribution transformer of a transformer area;
according to the above-mentioned district distributed clean energy time sequence dynamic data model and district total load time sequence dynamic data model, the reverse overload model of the district distribution transformer is constructed as follows:
P tr (t)＝P g (t)-P d (t)
Q tr (t)＝Q g (t)-Q d (t)
wherein S is tr For the rated capacity of the distribution transformer of the transformer area, epsilon (·) is a step function, and lambda- (t) is the reverse overload rate of the distribution transformer of the transformer area at the moment t.
S2, providing a distribution transformer reverse overload grade evaluation model, which specifically comprises the following steps:
s2.1, a distribution transformer burnout probability model;
the probability of the burning of the distribution transformer is related to the load rate and the duration time, and the greater the load rate and the longer the duration time are, the greater the burning probability is, and the scheme adopts a double-exponential model of the reverse load rate and the time integral thereof, namely:
wherein, xi (t) is the burning probability of the distribution transformer, e (·) As a function of the index of the values,is the maximum allowable value of reverse overload;
h (t) is the integral of the reverse overload rate of the distribution transformer over the duration, namely:
wherein t is 0 To reverse overload start time, h max Is the allowed maximum value of h (t).
S2.2, a reverse overload grade evaluation model of the distribution transformer;
according to the distribution transformer burnout probability model, the reverse overload grade of the distribution transformer is subjected to grading evaluation, and the evaluation model is as follows:
wherein r (t) is the reverse overload grade of the distribution transformer, and is sequentially divided into A according to the burning probability value of the distribution transformer + Seven grades, A, B, C, D, E, F, the higher the probability of a distribution transformer burn out, the higher the reverse overload grade.
S3, providing a configuration strategy of the distributed clean energy flexible rigid regulation device adapting to different distributed clean energy output characteristics and adjustable characteristics and the reverse overload level of the distribution transformer of the transformer area, wherein the configuration strategy specifically comprises the following steps:
s3.1, analyzing the output characteristics and the adjustable characteristics of the distributed clean energy of the area;
according to the above-mentioned distribution type clean energy cluster division and the active estimation result, and combining whether each distribution type clean energy inverter has a communication interface and a regulating function or not, or whether the clean energy power generation grid-connected point is configured with an intelligent breaker or not, analyzing distribution type clean energy output characteristics and adjustable characteristics of the distribution type clean energy, specifically as follows:
(1) Acquiring the partition and the active estimation result of the distributed clean energy clusters in the platform area;
(2) Aiming at each cluster, classifying the distributed clean energy inverters with communication interfaces and adjusting functions in the cluster into a group, namely an adjustable group;
(3) Aiming at each cluster, the distributed clean energy sources with the condition of installing the intelligent circuit breaker at the grid-connected point in the cluster are classified as a controllable unit.
S3.2, configuring a strategy of the flexible rigidity regulating device of the distributed clean energy;
different output characteristics and adjustable characteristics of the distributed clean energy and the reverse overload grade of the distribution transformer in the transformer area are considered, and a differential configuration strategy of the flexible rigid adjustment device of the distributed clean energy is provided, which is specifically as follows:
(1) According to the method for clustering the distributed clean energy sources in the transformer areas, in order to ensure the reference function of the marker post units in each cluster, the marker post units in each cluster are not used as adjustable units (comprising the adjustable units and the controllable units), namely the marker post units do not need to be provided with flexible or rigid adjusting and controlling devices;
(2) Reverse overload class A for distribution transformer + The power distribution transformer comprises a power distribution transformer, a power distribution transformer and a power distribution transformer, wherein the power distribution transformer comprises a power distribution transformer area and a power distribution transformer area of A, B, C, D, E, and the power distribution transformer area is characterized in that adjustable units in each distributed clean energy cluster are sequentially provided with a clean energy partner flexible adjusting device from large to small according to the adjustable capacity until the reverse overload level of the power distribution transformer is reduced to the level F; if the adjustable capacity of the adjustable units in each distributed clean energy cluster in the platform area is insufficient to enable the reverse overload level of the distribution transformer to be reduced to the F level, the controllable units in each distributed clean energy cluster in the platform area are sequentially provided with intelligent grid-connected point circuit breakers according to the controllable capacity from large to small until the reverse overload level of the distribution transformer is reduced to the F level;
(3) For a distribution transformer reverse overload grade F transformer area, each distributed clean energy source under the transformer area does not need to be provided with a flexible rigidity regulating device.
As shown in fig. 2, the present embodiment also provides a distributed clean energy flexible rigid regulation device platform, the device comprising a processor, a memory, and a computer program stored in the memory and running on the processor.
The processor comprises one or more than one processing core, the processor is connected with the memory through a bus, the memory is used for storing program instructions, and the configuration method of the distributed clean energy flexible rigid regulating device is realized when the processor executes the program instructions in the memory.
Alternatively, the memory may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
In addition, the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the method for configuring the distributed clean energy flexible rigidity regulating device when being executed by a processor.
Optionally, the present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the steps of the distributed clean energy flexible rigidity control apparatus configuration method of the above aspects.
It will be appreciated by those of ordinary skill in the art that the processes for implementing all or part of the steps of the above embodiments may be implemented by hardware, or may be implemented by a program for instructing the relevant hardware, and the program may be stored in a computer readable storage medium, where the above storage medium may be a read-only memory, a magnetic disk or optical disk, etc.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. The configuration method of the distributed clean energy flexible rigidity regulating device is characterized by comprising the following steps of:
s1, constructing a reverse overload model of a transformer in a power distribution station, which specifically comprises the following steps:
s1.1, constructing a platform region distributed clean energy time sequence dynamic data model: taking account of the fact that electric quantity data of all distributed clean energy sources under a station area are collected, firstly, providing a station area distributed clean energy source cluster dividing method based on the electric quantity data, providing a cluster power estimating method based on active monitoring/prediction of a marker post unit, and further establishing a station area distributed clean energy source time sequence power characteristic model;
s1.2, constructing a platform region full load time sequence dynamic data model: taking the fact that the distribution transformer of the platform area realizes active and reactive net load monitoring into consideration, according to the net load monitoring data of the distribution transformer of the platform area, combining the active and reactive data of the clean energy of the platform area, firstly constructing a platform area full load calculation model, and further constructing a platform area full load time sequence dynamic data model based on the platform area distributed clean energy time sequence power data and the platform area full load calculation result;
s1.3, constructing a reverse overload model of a distribution transformer of a transformer area;
s2, providing a distribution transformer reverse overload grade evaluation model, which specifically comprises the following steps:
s2.1, a distribution transformer burnout probability model;
s2.2, a reverse overload grade evaluation model of the distribution transformer;
s3, providing a configuration strategy of the distributed clean energy flexible rigid regulation device adapting to different distributed clean energy output characteristics and adjustable characteristics and the reverse overload level of the distribution transformer of the transformer area, wherein the configuration strategy specifically comprises the following steps:
s3.1, analyzing the output characteristics and the adjustable characteristics of the distributed clean energy of the area;
s3.2, configuring a strategy of the flexible rigid regulating device of the distributed clean energy.
2. The method for configuring a distributed clean energy flexible rigidity control apparatus according to claim 1, wherein: in the step S1.1, when the platform area distributed clean energy time sequence dynamic data model is constructed, the platform area distributed clean energy cluster dividing method based on the electric quantity data comprises the following steps:
let s i (T) is the unit installed active power quantity of the T-th metering interval of the distributed clean energy i, namely the active power quantity of the clean energy power generation divided by the installed capacity, and the unit installed active power quantity data set of the clean energy power generation in the T continuous metering intervals is as follows:
s i ＝[s i (1),s i (2),...,s i (T)]
the unit installed active power quantity of the nth metering interval of the N distributed clean energy sources under the platform area is recorded as S (t), and the unit installed active power quantity is expressed as follows:
S(t)＝[s 1 (t),s 2 (t),...,s N (t)]
the data set of the unit installed active power quantity of N distributed clean energy sources in T continuous metering intervals under the platform area is as follows:
S＝[s 1 ,s 2 ,...,s N ]
adopting a K-means clustering method, and dividing S into K clusters by taking the L2 norm distance as the minimum principle;
the L2 norm distance between the unit installed active power quantity data sets of the distributed clean energy sources a and b is as follows:
d ab ＝||s a -s b ||
wherein s is a 、s b And the active electricity quantity data sets are respectively installed for the distributed clean energy source a and the unit of the distributed clean energy source a in T continuous metering intervals.
3. The method for configuring the distributed clean energy flexible rigidity control device according to claim 2, wherein: in the step S1.1, when the platform-area distributed clean energy time sequence dynamic data model is constructed, the cluster power estimation method based on the active monitoring/prediction of the marker post unit comprises the following steps:
according to the K distributed clean energy clusters after clustering, a marker post unit is taken from each cluster K, and the similarity of the power generation capacity of the clean energy in one cluster is considered, so that a cluster power estimation model is as follows:
wherein P is k (t) is the active estimated value of the distributed clean energy cluster k at the time t,total capacity of the distributed clean energy cluster k, +.>For the active monitoring/prediction value of the marker post set of the distributed clean energy cluster k at the time t,the installed capacity of the marker post unit of the distributed clean energy cluster k; q (Q) k (t) is the reactive power estimated value of the distributed clean energy cluster k at the moment t, < ->Total capacity of inverter for distributed clean energy cluster k, +.>Reactive monitoring/planning value of the pole set at time t for distributed clean energy cluster k, +.>The inverter capacity of the marker post unit of the distributed clean energy cluster k;
furthermore, the method establishes a platform region distributed clean energy time sequence power data model as follows:
based on the power estimated value of the distributed clean energy cluster k at the time t, the time sequence active and reactive data of all the distributed clean energy under the platform area can be obtained, namely:
wherein P is g (t)、Q g And (t) the time sequence active and reactive data of all the distributed clean energy sources in the platform area at the time t respectively.
4. The method for configuring a distributed clean energy flexible rigidity control apparatus according to claim 3, wherein: in the step S1.2, when the platform area full load time sequence dynamic data model is constructed, the constructed platform area full load calculation model is as follows:
P d (t)＝P tr (t)+P g (t)
Q d (t)＝Q tr (t)+Q g (t)
wherein P is tr (t)、Q tr (t) are respectively the active and reactive monitoring values of the distribution transformer of the transformer area at the moment t, namely the active and reactive net loads of the transformer area, P d (t)、Q d And (t) respectively obtaining the full active load and the full reactive load of the platform region.
5. The method for configuring the distributed clean energy flexible rigidity control apparatus according to claim 4, wherein: in the step S1.2, the construction of the platform area full load time sequence dynamic data model specifically includes:
for active time sequence dynamic data of the total load of the platform area at the future moment, training and predicting by adopting a neural network model according to the active historical result of the total load of the platform area obtained by calculation and combining the distributed clean energy time sequence power data model of the platform area;
the input data trained by the neural network model are the real data of the time sequence of the clean energy source of the area region, the output data are the real data of the time sequence of the total load of the area region, and the trained neural network model is adopted to predict the real data of the time sequence of the total load of the area region, namely:
P d (t)＝net P (P g (t))
wherein net is P (. Cndot.) is the trained neural network model;
for reactive time sequence dynamic data of the total load of the transformer area at the future moment, according to the active data, the active and reactive historical calculation results of the total load of the transformer area are combined to estimate:
wherein Γ is the active power of P in the historical calculation result of the total load of the platform region d Data points of (t), Q d (τ) is the reactive data for the τ data point.
6. The method for configuring a distributed clean energy flexible rigidity control apparatus according to claim 5, wherein: in the step 1.3, according to the above-mentioned district distributed clean energy time sequence dynamic data model and district total load time sequence dynamic data model, the reverse overload model of the district distribution transformer is constructed as follows:
P tr (t)＝P g (t)-P d (t)
Q tr (t)＝Q g (t)-Q d (t)
wherein S is tr For rated capacity of distribution transformer in transformer area, epsilon (·) is step function, lambda - And (t) the reverse overload rate of the distribution transformer of the transformer area at the moment t.
7. The method for configuring a distributed clean energy flexible rigidity control apparatus according to claim 6, wherein: in the step S2.1, when the power distribution transformer burnout probability model is constructed, the probability of the power distribution transformer burnout is related to the load rate and the duration time, the larger the load rate is, the longer the duration time is, the larger the burnout probability is, and a double-exponential model of the reverse load rate and the time integral thereof is adopted, namely:
wherein, xi (t) is the burning probability of the distribution transformer, e (·) As a function of the index of the values,is the maximum allowable value of reverse overload;
h (t) is the integral of the reverse overload rate of the distribution transformer over the duration, namely:
wherein t is 0 To reverse overload start time, h max Is the allowed maximum value of h (t).
8. The method for configuring a distributed clean energy flexible rigidity control apparatus according to claim 7, wherein: in the step S2.2, when a distribution transformer reverse overload grade evaluation model is constructed, the distribution transformer reverse overload grade is rated according to the above distribution transformer burnout probability model, and the evaluation model is as follows:
wherein r (t) is the reverse overload grade of the distribution transformer, and is sequentially divided into A according to the burning probability value of the distribution transformer + Seven grades, A, B, C, D, E, F, the higher the probability of a distribution transformer burn out, the higher the reverse overload grade.
9. The method for configuring a distributed clean energy flexible rigidity control apparatus according to claim 8, wherein: in the step S3.1, when analyzing the output characteristics and the adjustable characteristics of the district distributed clean energy, according to the distribution and active estimation results of the district distributed clean energy clusters, and by combining whether each distributed clean energy inverter has a communication interface and an adjusting function or whether the power generation grid-connected point of the clean energy is configured with an intelligent breaker, analyzing the output characteristics and the adjustable characteristics of the district distributed clean energy, specifically as follows:
(1) Acquiring the partition and the active estimation result of the distributed clean energy clusters in the platform area;
(2) Aiming at each cluster, classifying the distributed clean energy inverters with communication interfaces and adjusting functions in the cluster into a group, namely an adjustable group;
(3) Aiming at each cluster, the distributed clean energy sources with the condition of installing the intelligent circuit breaker at the grid-connected point in the cluster are classified as a controllable unit.
10. The method for configuring a distributed clean energy flexible rigidity control apparatus according to claim 9, wherein: in step S3.2, a differential configuration strategy of the flexible rigid regulation device for the distributed clean energy is proposed by considering different output characteristics and adjustable characteristics of the distributed clean energy and the reverse overload level of the distribution transformer of the transformer area, and is specifically as follows:
(1) According to the method for grouping the distributed clean energy sources in the transformer areas, in order to ensure the reference function of the marker post units in each cluster, the marker post units in each cluster are not used as adjustable units, namely, the marker post units do not need to be provided with flexible or rigid adjusting and controlling devices;
(2) Reverse overload class A for distribution transformer + The power distribution transformer comprises a power distribution transformer, a power distribution transformer and a power distribution transformer, wherein the power distribution transformer comprises a power distribution transformer area and a power distribution transformer area of A, B, C, D, E, and the power distribution transformer area is characterized in that adjustable units in each distributed clean energy cluster are sequentially provided with a clean energy partner flexible adjusting device from large to small according to the adjustable capacity until the reverse overload level of the power distribution transformer is reduced to the level F; if the adjustable capacity of the adjustable units in each distributed clean energy cluster in the platform area is insufficient to enable the reverse overload level of the distribution transformer to be reduced to the F level, the controllable units in each distributed clean energy cluster in the platform area are sequentially provided with intelligent grid-connected point circuit breakers according to the controllable capacity from large to small until the reverse overload level of the distribution transformer is reduced to the F level;
(3) For a distribution transformer reverse overload grade F transformer area, each distributed clean energy source under the transformer area does not need to be provided with a flexible rigidity regulating device.
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