CN114792993A - Distributed photovoltaic access prediction method, system and device - Google Patents

Distributed photovoltaic access prediction method, system and device Download PDF

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CN114792993A
CN114792993A CN202210716011.4A CN202210716011A CN114792993A CN 114792993 A CN114792993 A CN 114792993A CN 202210716011 A CN202210716011 A CN 202210716011A CN 114792993 A CN114792993 A CN 114792993A
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CN114792993B (en
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钱琪
马超
夏宗阳
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Nanjing Hanyuan Technology Co ltd
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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
    • H02J3/381—Dispersed generators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
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    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • 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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    • 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
    • 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]
    • 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
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin

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Abstract

The invention provides a distributed photovoltaic access prediction method, a system and a device, wherein the method comprises the following steps: acquiring photovoltaic station data, power load data, power grid equipment data and weather related data; determining a representative day according to the photovoltaic station data, the power load data and the weather related data, and calculating the load at each moment of the representative day and the photovoltaic output at each moment of the representative day; updating the equivalent impedance data of the equipment according to the power grid equipment data; carrying out load flow calculation according to active power and reactive power of the photovoltaic station inverter side, load at each representative day, photovoltaic output at each representative day and equipment equivalent impedance data to obtain a calculation result of each equipment at each moment; and comparing the calculation result of each device at each moment with a preset value to determine abnormal devices. The method can simulate the running state of the power grid after the distributed photovoltaic grid connection and pre-judge abnormal equipment in advance, thereby providing more scientific and accurate evaluation basis for operation and maintenance personnel.

Description

Distributed photovoltaic access prediction method, system and device
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a distributed photovoltaic access prediction method, a system and a device.
Background
Due to the randomness and uncertainty of the wind-solar power generation output, the power flow and harmonic waves of a power grid are difficult to predict after the distributed energy is connected to the grid. With the use proportion of the distributed energy resource machine being higher and higher, many problems are inevitably brought to the planning, operation and protection of the power system.
At present, the proportion of distributed photovoltaic in China is small, and photovoltaic access planning work is mostly carried out according to the requirement of 'connecting as soon as possible'. However, as the proportion of distributed energy resources increases, the power grid is affected more and more by fluctuations. When the distributed energy is operated in a large-scale grid-connected mode, the safety and stability of the power system are seriously influenced.
In summary, a rigorous and scientific access prediction method is needed to assist the power grid planning work.
Disclosure of Invention
The invention aims to provide a distributed photovoltaic access prediction method, a distributed photovoltaic access prediction system and a distributed photovoltaic access prediction device, which can be used for simulating the running state of a power grid after distributed photovoltaic grid connection and predicting abnormal equipment in advance, so that more scientific and accurate evaluation basis is provided for operation and maintenance personnel.
In order to achieve the purpose, the invention provides the following technical scheme: a distributed photovoltaic access prediction method comprises the following steps:
with respect to one of the prediction periods, the prediction period,
acquiring photovoltaic station data, power load data, power grid equipment data and weather related data;
the photovoltaic station data comprise active power, reactive power, daily generated energy, capacity of a single photovoltaic power station, photovoltaic output efficiency coefficient and power factor of the photovoltaic station inverter side;
the power grid equipment data comprise distribution transformer active power, distribution transformer reactive power, transformer no-load current percentage, transformer no-load loss, transformer capacity, transformer voltage, transformer short-circuit voltage percentage, transformer short-circuit loss, outer section area of a conductor, line length, conductor resistivity, conductor splitting number, conductor geometric mean distance and split conductor equivalent radius;
the power load data comprises user active power, user reactive power and user daily electricity;
the weather related data comprises temperature, humidity, solar daily radiant quantity, radiation illuminance under standard conditions and actual radiation illuminance;
determining a representative day according to the photovoltaic station data, the power load data and the weather related data;
calculating the load at each moment of the representative day and the photovoltaic output at each moment of the representative day;
updating the equivalent impedance data of the equipment according to the power grid equipment data;
carrying out load flow calculation according to active power, reactive power, load at each time of a representative day, photovoltaic output at each time of the representative day and equivalent impedance data of the equipment to obtain the active power, reactive power and voltage of each equipment at each time;
and comparing the active power, the reactive power and the voltage of each device with preset values at each moment to determine the devices with abnormal operation.
Further, the photovoltaic station data, the power load data and the power grid equipment data are obtained by mutually associating a client side marketing system and a production side pms system through a fuzzy matching method.
Further, the user active power and the user reactive power comprise low-voltage user active power, low-voltage user reactive power, medium-voltage user active power and medium-voltage user reactive power;
the active power of the medium-voltage users and the reactive power of the medium-voltage users are obtained through a client-side marketing system;
the active power and the reactive power of the low-voltage users are obtained by the following methods:
acquiring 24-moment fluctuation curves of daily freezing electric quantity of low-voltage users and head end power of a distribution area in which the low-voltage users are located in a client-side marketing system;
respectively calculating the electric quantity ratio of the low-voltage user at each moment according to the fluctuation curve;
and calculating to obtain the active power and the reactive power of the low-voltage user according to the electric quantity ratio at each moment and the daily frozen electric quantity of the low-voltage user.
Further, the determining a representative day from the photovoltaic station data, the electrical load data, and the weather-related data comprises:
and (3) calculating the sunlight photovoltaic transmission degree index in the prediction period, wherein the calculation formula is as follows:
Figure 759243DEST_PATH_IMAGE001
，
Figure 863334DEST_PATH_IMAGE002
Δ E represents an index of the degree of solar photovoltaic turnover, in kWh/day, E pvstation Indicating daily generated energy in kWh/day, E pq Representing the daily electricity consumption of the user in kWh/day, H day Represents the solar daily radiant quantity, and the unit is kWh/(m) 2 ·day)，P pvstation Representing the capacity of a single photovoltaic power station in kW, E a Represents the irradiance under standard conditions, and has the unit of kWh/(m) 2 ) K represents a photovoltaic output efficiency coefficient;
and selecting the day when the sunlight photovoltaic delivery degree index reaches the maximum value in the prediction period as a representative day.
Further, the calculating the representative daily load and the representative daily photovoltaic output at each time comprises:
selecting user active power, user reactive power, distribution transformer active power and distribution transformer reactive power in a plurality of days in which the similarity of data related to the representative weather image in each preset time before and after the representative day is in a preset range;
aiming at each moment, respectively obtaining data of clustering center points of each user and each distribution transformer at 24 moments by adopting a k-means clustering algorithm according to the selected user active power, user reactive power, distribution transformer active power and distribution transformer reactive power, and taking the data as the load of each moment of a representative day;
calculating the photovoltaic active power and the photovoltaic reactive power of each representative day at each moment according to the capacity of a single photovoltaic power station, the radiation illumination, the photovoltaic output efficiency coefficient, the actual radiation illumination and the power factor under the standard conditionAnd the photovoltaic output is taken as a representative day photovoltaic output at each moment, and the calculation formula of the photovoltaic active power is as follows:
Figure 580755DEST_PATH_IMAGE003
the calculation formula of the photovoltaic reactive power is as follows:
Figure 897335DEST_PATH_IMAGE004
，H hour representing the actual irradiance in kWh/(m) 2 Hour), cos φ is the power factor.
Further, the updating of the device equivalent impedance data according to the power grid device data includes:
carrying out convergence correction on the power grid equipment data;
and calculating and updating the updated value of the equipment equivalent impedance data according to the corrected power grid equipment data.
Further, the calculating and updating the updated value of the device equivalent impedance data according to the corrected power grid device data includes:
updating and calculating the impedance of the transformer, wherein the calculation formula is as follows:
Figure 383811DEST_PATH_IMAGE005
，
Figure 399084DEST_PATH_IMAGE006
，
Figure 603800DEST_PATH_IMAGE007
，
Figure 989651DEST_PATH_IMAGE008
，R T the equivalent resistance of the transformer is represented, and the unit is omega; x T The equivalent reactance of the transformer is expressed in the unit of omega; g T The equivalent conductance of the transformer is expressed in the unit of S; b T The equivalent susceptance of the transformer is represented, and the unit is S; s N Represents the transformer capacity in MVA; u shape N Represents the transformer voltage in kV; delta P S Represents short circuit loss in kW; u shape S % represents percent voltage; delta P 0 Expressing no-load loss, and the unit is kW; i is S % indicates percent no-load current;
the line impedance is updated and calculated, and the calculation formula is as follows:
Figure 596213DEST_PATH_IMAGE009
，
Figure 510948DEST_PATH_IMAGE010
，
Figure 202961DEST_PATH_IMAGE011
，
Figure 736710DEST_PATH_IMAGE012
，R L represents the equivalent resistance of the line with the unit of omega, X L Represents the equivalent reactance of the line, and the unit is omega, S represents the external cross-sectional area of the wire, and the unit is mm 2 And ρ represents the wire resistivity in Ω. mm 2 N denotes the number of splits, D ge Represents the geometric mean spacing of the wires in mm, r eq Representing the equivalent radius of the split conductor in mm, G L Representing the equivalent conductance of the line in units of S, B L The equivalent susceptance of the line is expressed in units of S, L represents the length of the line, and the unit is km.
Further, the step of comparing the active power, the reactive power and the voltage of each device at each moment with preset values to determine the devices with abnormal operation includes:
representing the abnormal statistics of time of day, comprising: the system comprises a time division line total active power, a time division line total reactive power, a time division line load ratio, whether a time division line is overloaded or not, a time division distribution transformation total active power, a time division distribution transformation total reactive power, a time division distribution transformation current, a time division distribution transformation voltage, a time division distribution transformation load ratio, whether a time division distribution transformation is overvoltage or not, whether a time division distribution transformation is overloaded or not, whether a time division user total active power, a time division user total reactive power, a time division user current, a time division user voltage and whether a time division user is overvoltage or not;
statistics of day-to-day abnormalities, including: the method comprises the following steps of total active power of an all-day line, total reactive power of the all-day line, daily average line load rate, number of heavy load hours of the all-day line, total active power of an all-day distribution transformer, total reactive power of the all-day distribution transformer, upper limit hours of voltage of an all-day distribution piece, heavy load hours of the all-day distribution transformer, comprehensive problems of the all-day distribution transformer, total active power of an all-day user, total reactive power of an all-day user and upper limit hours of voltage of the all-day user.
The invention also provides a distributed photovoltaic access prediction system, which comprises:
the acquisition module is used for acquiring photovoltaic station data, power load data, power grid equipment data and meteorological related data;
the photovoltaic station data comprise active power, reactive power, daily generated energy, capacity of a single photovoltaic power station, photovoltaic output efficiency coefficient and power factor of the photovoltaic station inverter side;
the power grid equipment data comprise distribution transformer active power, distribution transformer reactive power, transformer no-load current percentage, transformer no-load loss, transformer capacity, transformer voltage, transformer short-circuit voltage percentage, transformer short-circuit loss, outer section area of a conductor, line length, conductor resistivity, conductor splitting number, conductor geometric mean distance and split conductor equivalent radius;
the power load data comprises user active power, user reactive power and user daily electricity consumption;
the weather related data comprises temperature, humidity, solar daily radiant quantity, radiation illuminance under standard conditions and actual radiation illuminance;
the representative day determining module is used for determining a representative day according to the photovoltaic station data, the power load data and the weather related data;
the representative day data calculation module is used for calculating the load of each time of a representative day and the photovoltaic output of each time of the representative day;
the updating calculation module is used for updating the equivalent impedance data of the equipment according to the power grid equipment data;
the load flow calculation module is used for carrying out load flow calculation according to the active power, the reactive power, the load at each time of a representative day, the photovoltaic output at each time of the representative day and the equivalent impedance data of the equipment to obtain the active power, the reactive power and the voltage of each equipment at each time;
and the abnormity judgment module is used for comparing the active power, the reactive power and the voltage of each device at each moment with preset values to determine the devices with abnormal operation.
The invention also provides a distributed photovoltaic access prediction device which comprises a processor and a memory, wherein the memory stores a computer program, and the steps of the method are realized when the processor executes the computer program.
Compared with the prior art, the invention has the beneficial effects that:
1. the method can simulate the running state of the power grid after the distributed photovoltaic grid connection and pre-judge abnormal equipment in advance, thereby providing more scientific and accurate evaluation basis for operation and maintenance personnel.
2. According to the invention, a client side marketing system and a production side pms system can be communicated, user data and equipment data are obtained by using a fuzzy algorithm, so that source data can be obtained more timely and accurately, and reactive power and active power of a low-voltage user are simulated by using daily frozen electric quantity of the low-voltage user, so that the defect that the low-voltage user cannot directly obtain the active power and the reactive power is overcome.
3. According to the method, the most serious photovoltaic inverted day in the photovoltaic planning area in one year is used as the representative day, and the data of a plurality of days similar to the weather data of the representative day in the prediction period is used as the input of the clustering algorithm, so that the accuracy and typicality of the data can be improved.
4. The invention takes the voltage and the load rate of the actual operation process of the power grid as the judgment standard of the equipment abnormity, and realizes the simulation of the operation state of the power grid.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1, a distributed photovoltaic access prediction method includes the following steps:
for a prediction period, such as six months or a year, the following operations are performed:
and S1, acquiring photovoltaic station data, electric load data, power grid equipment data and weather related data.
1. The photovoltaic station data comprise active power, reactive power, daily generated energy, capacity of a single photovoltaic power station, photovoltaic output efficiency coefficient and power factor of the photovoltaic station inverter side. The distributed photovoltaic station comprises:
photovoltaic station of enterprise and public institution: and acquiring association relation between the photovoltaic installation information applied by the 10 KV feeder line special transformer user in the client side marketing system and the medium-voltage user access point in the production side pms system to acquire photovoltaic station data of the medium-voltage user access point.
Rural area, public transformer station district photovoltaic station: and acquiring the photovoltaic installation information of the low-voltage users under the 0.38 kilovolt distribution area in the client side marketing system and the low-voltage user access points in the pms system at the production side to acquire photovoltaic station data of the low-voltage user access points.
2. The power load data comprises user active power, user reactive power and user daily electricity consumption.
In the power flow model, the electrical loads are treated as PQ nodes. Specifically, the method comprises the following steps:
medium-pressure load PQ value: and corresponding to a medium-voltage user access point in the pms system of the production side, wherein the voltage grade is 10 kilovolts, and the active power and the reactive power of medium-voltage users of the type of users in the marketing system of the client side are obtained.
Low-voltage load PQ value: the method is obtained by adopting a simulation calculation mode, and specifically comprises the following steps:
the voltage level of a low-voltage user access point in the pms system on the production side is 0.38 kilovolt, and 24-moment fluctuation curves of the daily frozen electric quantity of the low-voltage user and the head end power of the transformer area in which the low-voltage user is located in the marketing system on the client side are obtained.
And respectively calculating the electric quantity ratio of the low-voltage user at each moment according to the fluctuation curve.
And calculating active power and reactive power of the low-voltage users, namely the low-voltage load PQ value according to the electric quantity ratio at each moment and the daily frozen electric quantity of the low-voltage users.
3. Acquiring power grid equipment data from a production side pms system, wherein the power grid equipment data comprises the following steps:
and the distribution transformation data comprises distribution transformation active power, distribution transformation reactive power, transformer no-load current percentage, transformer no-load loss, transformer capacity, transformer voltage, transformer short-circuit voltage percentage and transformer short-circuit loss.
And the line data comprises the outer sectional area of the conductor, the length of the line, the resistivity of the conductor, the number of split conductors, the geometric uniform distance of the conductor and the equivalent radius of the split conductor.
4. Weather related data is obtained from a weather system, and comprises temperature, humidity, solar daily radiation amount, radiation illumination under standard conditions and actual radiation illumination.
And S2, determining a representative day according to the photovoltaic station data, the electric load data and the weather related data.
The method specifically comprises the following steps:
and S21, calculating the photovoltaic backward-conveying degree index according to the daily generated energy, the capacity of a single photovoltaic power station, the photovoltaic output efficiency coefficient, the radiation illumination under the standard condition, the solar daily radiant quantity and the user daily electric quantity.
S22, taking the day when the sunlight photovoltaic delivery degree index reaches the maximum value as a representative day, and the calculation formula is as follows:
Figure 447046DEST_PATH_IMAGE001
，
Figure 548994DEST_PATH_IMAGE002
Δ E represents the index of the degree of solar photovoltaic collapse in kWh/day, E pvstation Indicating daily generated energy in kWh/day, E pq Indicating the daily electric quantity of the userIn units of kWh/day, H day Expressing the solar radiation amount, and the unit is kWh/(m) 2 ·day)，P pvstation Representing the capacity of a single photovoltaic power station in kW, E a Represents the irradiance under standard conditions, and has a unit of kWh/(m) 2 ) And K represents a photovoltaic output efficiency coefficient.
And S3, calculating the load of each time of the representative day and the photovoltaic output of each time of the representative day. The method comprises the following steps:
and S31, selecting the active power of the user, the reactive power of the user, the distribution transformer active power and the distribution transformer reactive power in a plurality of days with the similarity of the data related to the representative weather within the preset range in each preset time before and after the representative day. In this example, the preset time is 15 days, and the preset range is ± 10 days.
And S32, aiming at each moment, adopting a k-means clustering algorithm according to the selected user active power, user reactive power, distribution transformer active power and distribution transformer reactive power, setting the clustering number to be 1, respectively obtaining the data of the clustering center points of each user and each distribution transformer at 24 moments, and taking the data as the load of each moment of the representative day.
S33, calculating photovoltaic active power and photovoltaic reactive power at each representative day according to the capacity of a single photovoltaic power station, the radiation illumination under standard conditions, the photovoltaic output efficiency coefficient, the actual radiation illumination and the power factor, and taking the photovoltaic active power and the photovoltaic reactive power as the photovoltaic output at each representative day, wherein the calculation formula of the photovoltaic active power is as follows:
Figure 977571DEST_PATH_IMAGE003
the calculation formula of the photovoltaic reactive power is as follows:
Figure 190377DEST_PATH_IMAGE004
，H hour representing the actual irradiance in kWh/(m) 2 Hour), cos φ is the power factor.
And S4, updating the equivalent impedance data of the equipment according to the power grid equipment data. The method specifically comprises the following steps:
and S41, performing convergence correction on the power grid equipment data according to the industry specifications. The following is specifically referred to:
Figure 755220DEST_PATH_IMAGE013
and S42, calculating and updating the updated value of the equipment equivalent impedance data according to the corrected power grid equipment data. The method specifically comprises the following steps:
1. the impedance of the transformer is updated and calculated, and the calculation formula is as follows:
Figure 28069DEST_PATH_IMAGE005
，
Figure 678362DEST_PATH_IMAGE006
，
Figure 694860DEST_PATH_IMAGE007
，
Figure 379788DEST_PATH_IMAGE008
，R T the equivalent resistance of the transformer is represented, and the unit is omega; x T The equivalent reactance of the transformer is expressed in the unit of omega; g T The equivalent conductance of the transformer is expressed in the unit of S; b T The equivalent susceptance of the transformer is represented, and the unit is S; s. the N Represents the transformer capacity in MVA; u shape N Represents the transformer voltage in kV; delta P S Represents short circuit loss in kW; u shape S % represents percent voltage; delta P 0 Indicating no-load loss, with the unit of kW; i is S % represents percent no-load current;
2. and (3) updating and calculating the line impedance, wherein the calculation formula is as follows:
Figure 948172DEST_PATH_IMAGE009
，
Figure 570915DEST_PATH_IMAGE010
，
Figure 634511DEST_PATH_IMAGE011
，
Figure 659099DEST_PATH_IMAGE012
，R L represents the equivalent resistance of the line with the unit of omega, X L Represents the equivalent reactance of the line, and the unit is omega, S represents the external cross-sectional area of the wire, and the unit is mm 2 And ρ represents the wire resistivity in Ω. mm 2 N denotes the number of splits, D ge Represents the geometric mean spacing of the wires in mm, r eq Representing the equivalent radius of the split conductor in mm, G L Representing the equivalent conductance of the line in units S, B L The equivalent susceptance of the line is represented in units of S, L represents the length of the line, and the unit is km.
And S5, carrying out load flow calculation according to the active power and the reactive power of the inverter side of the photovoltaic station, the load at each time of the representative day, the photovoltaic output at each time of the representative day and the equivalent impedance data of the equipment to obtain the active power, the reactive power and the voltage of each equipment at each time. When carrying out load flow calculation, the data of each photovoltaic node is processed as a PQ node.
And S6, comparing the active power, the reactive power and the voltage of each device at each moment with preset values, and determining the devices with abnormal operation. The method specifically comprises the following steps:
representing daily time anomaly statistics, comprising: the system comprises a time division line total active power, a time division line total reactive power, a time division line load rate, whether a time division line is overloaded or not, a time division distribution transformation total active power, a time division distribution transformation total reactive power, a time division distribution transformation current, a time division distribution transformation voltage, a time division distribution transformation load rate, whether a time division distribution transformation is overvoltage or not, whether a time division distribution transformation is overloaded or not, a time division user total active power, a time division user total reactive power, a time division user current, a time division user voltage and whether a time division user is overvoltage or not.
Representative day-wide anomaly statistics, including: the total active power of the all-day line, the total reactive power of the all-day line, the daily average line load rate, the number of heavy load hours of the all-day line, the total active power of the all-day distribution transformer, the total reactive power of the all-day distribution transformer, the number of upper limit hours of the all-day distribution transformer voltage, the number of heavy load hours of the all-day distribution transformer, the comprehensive problem of the all-day distribution transformer, the total active power of the all-day user, the total reactive power of the all-day user and the number of upper limit hours of the all-day user voltage.
The invention also provides a distributed photovoltaic access prediction system, which comprises:
the acquisition module is used for acquiring photovoltaic station data, power load data, power grid equipment data and meteorological related data;
the photovoltaic station data comprise active power, reactive power, daily generated energy, capacity of a single photovoltaic power station, photovoltaic output efficiency coefficient and power factor of the photovoltaic station inverter side;
the power grid equipment data comprise distribution transformer active power, distribution transformer reactive power, transformer no-load current percentage, transformer no-load loss, transformer capacity, transformer voltage, transformer short-circuit voltage percentage, transformer short-circuit loss, outer section area of a conductor, line length, conductor resistivity, conductor splitting number, conductor geometric uniform distance and split conductor equivalent radius;
the power load data comprises user active power, user reactive power and user daily electricity consumption;
the weather related data comprises temperature, humidity, solar daily radiant quantity, radiation illuminance under standard conditions and actual radiation illuminance;
the representative day determining module is used for determining a representative day according to the photovoltaic station data, the power load data and the weather related data;
the representative day data calculation module is used for calculating the load at each moment of the representative day and the photovoltaic output at each moment of the representative day;
the updating calculation module is used for updating the equivalent impedance data of the equipment according to the power grid equipment data;
the load flow calculation module is used for carrying out load flow calculation according to the active power, the reactive power, the load at each time of a representative day, the photovoltaic output at each time of the representative day and the equivalent impedance data of the equipment to obtain the active power, the reactive power and the voltage of each equipment at each time;
and the abnormity judgment module is used for comparing the active power, the reactive power and the voltage of each device at each moment with preset values to determine the devices with abnormal operation.
The acquisition module further comprises:
and the acquisition submodule is used for acquiring 24-moment fluctuation curves of the daily freezing electric quantity of the low-voltage users and the head end power of the distribution area in which the low-voltage users are located in the client-side marketing system.
And the proportion calculation submodule is used for respectively calculating the electric quantity proportion of the low-voltage user at each moment according to the fluctuation curve.
And the user load calculation submodule is used for calculating the active power and the reactive power of the low-voltage user according to the electric quantity ratio at each moment and the daily freezing electric quantity of the low-voltage user.
The representative day determination module includes:
the sunlight photovoltaic delivery degree calculating submodule is used for calculating a sunlight photovoltaic delivery degree index in a prediction period, and the calculation formula is as follows:
Figure 788598DEST_PATH_IMAGE001
，
Figure 164216DEST_PATH_IMAGE002
Δ E represents an index of the degree of solar photovoltaic turnover, in kWh/day, E pvstation Indicating daily generated energy in kWh/day, E pq Representing the daily electricity consumption of the user in kWh/day, H day Represents the solar daily radiant quantity, and the unit is kWh/(m) 2 ·day)，P pvstation Representing the capacity of a single photovoltaic power station in kW, E a Represents the irradiance under standard conditions, and has a unit of kWh/(m) 2 ) And K represents a photovoltaic output efficiency coefficient.
And the representative day determining submodule is used for selecting the day when the sunlight photovoltaic transmission degree index reaches the maximum value in the prediction period as the representative day.
The representative day data calculation module comprises:
and the data selection submodule is used for selecting the user active power, the user reactive power, the distribution transformer active power and the distribution transformer reactive power in a plurality of days in which the similarity of the data related to the representative weather in the preset range in each preset time before and after the representative day.
And the photovoltaic load calculation submodule is used for respectively obtaining data of the clustering center points of each user and each distribution transformer at 24 moments by adopting a k-means clustering algorithm according to the selected user active power, user reactive power, distribution transformer active power and distribution transformer reactive power at each moment, and taking the data as the load at each moment of the representative day.
The photovoltaic output calculation submodule is used for calculating photovoltaic active power and photovoltaic reactive power at each representative day according to the capacity of a single photovoltaic power station, the radiation illumination under standard conditions, the photovoltaic output efficiency coefficient, the actual radiation illumination and the power factor, and taking the photovoltaic active power and the photovoltaic reactive power as the photovoltaic output at each representative day, wherein the calculation formula of the photovoltaic active power is as follows:
Figure 771784DEST_PATH_IMAGE003
the calculation formula of the photovoltaic reactive power is as follows:
Figure 916457DEST_PATH_IMAGE004
，H hour represents the actual radiation illuminance and has the unit of kWh/(m) 2 Hour), cos φ is the power factor.
The update calculation module includes:
and the convergence correction submodule is used for performing convergence correction on the power grid equipment data according to the industry specification. The following is specifically referred to:
Figure 216857DEST_PATH_IMAGE013
and the equipment equivalent impedance calculation submodule is used for calculating and updating the updated value of the equipment equivalent impedance data according to the corrected power grid equipment data. Further comprising:
the transformer impedance updating calculation submodule is used for calculating an updating value of the transformer impedance, and the calculation formula is as follows:
Figure 79771DEST_PATH_IMAGE005
，
Figure 100817DEST_PATH_IMAGE006
，
Figure 83685DEST_PATH_IMAGE007
，
Figure 305719DEST_PATH_IMAGE008
，R T the equivalent resistance of the transformer is represented, and the unit is omega; x T The equivalent reactance of the transformer is expressed in the unit of omega; g T The equivalent conductance of the transformer is expressed in the unit of S; b T The equivalent susceptance of the transformer is represented, and the unit is S; s N Represents the transformer capacity in MVA; u shape N Represents the transformer voltage in kV; delta P S Representing short circuit loss in kW; u shape S % represents percent voltage; delta P 0 Indicating no-load loss, with the unit of kW; I.C. A S % represents percent no-load current;
the line impedance updating calculation submodule is used for calculating an updating value of line impedance, and the calculation formula is as follows:
Figure 639617DEST_PATH_IMAGE009
，
Figure 605299DEST_PATH_IMAGE010
，
Figure 708253DEST_PATH_IMAGE011
，
Figure 835609DEST_PATH_IMAGE012
，R L representing the equivalent resistance of the line in units of omega, X L Represents the equivalent reactance of the line, the unit is omega, S represents the external section area of the wire, the unit is mm 2 And ρ represents the wire resistivity in Ω. mm 2 N denotes the number of splits, D ge Represents the geometric mean spacing of the wires in mm, r eq Representing the equivalent radius of the split conductor in mm, G L Representing the equivalent conductance of the line in units of S, B L The equivalent susceptance of the line is shown, the unit is S, L represents the length of the lineThe unit is km.
The invention also provides a distributed photovoltaic access prediction device which comprises a processor and a memory, wherein the memory stores a computer program, and the steps of the method are realized when the processor executes the computer program.
The details of the present invention are well known to those skilled in the art.
As will be appreciated by one skilled in the art, 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 above is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, it is possible to make several improvements and modifications without departing from the technical principle of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention.

Claims (10)

1. A distributed photovoltaic access prediction method is characterized by comprising the following steps: the method comprises the following steps:
with respect to one of the prediction periods, the prediction period,
acquiring photovoltaic station data, power load data, power grid equipment data and weather related data;
the photovoltaic station data comprises active power, reactive power, daily generated energy, capacity of a single photovoltaic power station, photovoltaic output efficiency coefficient and power factor of the photovoltaic station inverter side;
the power grid equipment data comprise distribution transformer active power, distribution transformer reactive power, transformer no-load current percentage, transformer no-load loss, transformer capacity, transformer voltage, transformer short-circuit voltage percentage, transformer short-circuit loss, outer section area of a conductor, line length, conductor resistivity, conductor splitting number, conductor geometric uniform distance and split conductor equivalent radius;
the power load data comprises user active power, user reactive power and user daily electricity;
the weather related data comprises temperature, humidity, solar daily radiant quantity, radiation illuminance under standard conditions and actual radiation illuminance;
determining a representative day according to the photovoltaic station data, the power load data and the weather related data;
calculating the load at each moment of the representative day and the photovoltaic output at each moment of the representative day;
updating the equivalent impedance data of the equipment according to the power grid equipment data;
carrying out load flow calculation according to active power, reactive power, load at each time of a representative day, photovoltaic output at each time of the representative day and equivalent impedance data of the equipment to obtain the active power, reactive power and voltage of each equipment at each time;
and comparing the active power, the reactive power and the voltage of each device with preset values at each moment to determine the devices with abnormal operation.
2. The distributed photovoltaic access prediction method of claim 1, characterized in that: and the photovoltaic station data, the power load data and the power grid equipment data are obtained by mutually associating a client side marketing system and a production side pms system through a fuzzy matching method.
3. The distributed photovoltaic access prediction method according to claim 2, characterized in that: the user active power and the user reactive power comprise low-voltage user active power, low-voltage user reactive power, medium-voltage user active power and medium-voltage user reactive power;
the active power of the medium-voltage users and the reactive power of the medium-voltage users are obtained through a client-side marketing system;
the active power and the reactive power of the low-voltage users are obtained by the following methods:
acquiring 24-moment fluctuation curves of daily freezing electric quantity of low-voltage users and head end power of a distribution area in which the low-voltage users are located in a client-side marketing system;
respectively calculating the electric quantity ratio of the low-voltage user at each moment according to the fluctuation curve;
and calculating to obtain the active power and the reactive power of the low-voltage user according to the electric quantity ratio at each moment and the daily frozen electric quantity of the low-voltage user.
4. The distributed photovoltaic access prediction method of claim 3, characterized in that: the determining a representative day according to the photovoltaic station data, the electrical load data and the weather related data comprises:
and (3) calculating the sunlight photovoltaic transmission degree index in the prediction period, wherein the calculation formula is as follows:
Figure 669903DEST_PATH_IMAGE001
，
Figure 621941DEST_PATH_IMAGE002
Δ E represents an index of the degree of solar photovoltaic turnover, in kWh/day, E pvstation Indicating daily generated energy in kWh/day, E pq The daily electricity consumption of the user is represented by kWh/day, H day Expressing the solar radiation amount, and the unit is kWh/(m) 2 ·day)，P pvstation Representing the capacity of a single photovoltaic power plant in kW, E a Represents the irradiance under standard conditions, and has a unit of kWh/(m) 2 ) K represents a photovoltaic output efficiency coefficient;
and selecting the day when the sunlight photovoltaic delivery degree index reaches the maximum value in the prediction period as a representative day.
5. The distributed photovoltaic access prediction method of claim 4, wherein: the calculation of the representative day load and the representative day photovoltaic output at each moment comprises the following steps:
selecting user active power, user reactive power, distribution transformer active power and distribution transformer reactive power in a plurality of days in which the similarity of data related to the representative weather image in each preset time before and after the representative day is in a preset range;
aiming at each moment, respectively obtaining data of a clustering center point of each user and each distribution transformer at 24 moments by adopting a k-means clustering algorithm according to the selected user active power, user reactive power, distribution transformer active power and distribution transformer reactive power, and taking the data as the load of each moment of a representative day;
calculating the photovoltaic active power and the photovoltaic reactive power of each representative day at each moment according to the capacity of a single photovoltaic power station, the radiation illumination, the photovoltaic output efficiency coefficient, the actual radiation illumination and the power factor under the standard condition, taking the photovoltaic active power and the photovoltaic reactive power as the photovoltaic output of each representative day at each moment, wherein the calculation formula of the photovoltaic active power is as follows:
Figure 785069DEST_PATH_IMAGE003
the calculation formula of the photovoltaic reactive power is as follows:
Figure 764526DEST_PATH_IMAGE004
，H hour represents the actual radiation illuminance and has the unit of kWh/(m) 2 Hour), cos φ is the power factor.
6. The distributed photovoltaic access prediction method according to claim 1, characterized in that: the updating of the equipment equivalent impedance data according to the power grid equipment data comprises the following steps:
carrying out convergence correction on the power grid equipment data;
and calculating and updating the updated value of the equivalent impedance data of the equipment according to the corrected power grid equipment data.
7. The distributed photovoltaic access prediction method of claim 6, wherein: the calculating and updating the updated value of the equivalent impedance data of the equipment according to the corrected data of the power grid equipment comprises the following steps:
the impedance of the transformer is updated and calculated, and the calculation formula is as follows:
Figure 278684DEST_PATH_IMAGE005
,
Figure 267369DEST_PATH_IMAGE006
,
Figure 285003DEST_PATH_IMAGE007
,
Figure 966520DEST_PATH_IMAGE008
， R T the equivalent resistance of the transformer is expressed, and the unit is omega; x T The equivalent reactance of the transformer is expressed in the unit of omega; g T The equivalent conductance of the transformer is expressed in the unit of S; b T The equivalent susceptance of the transformer is represented, and the unit is S; s. the N Represents the transformer capacity in MVA; u shape N Represents the transformer voltage in kV; delta P S Representing short circuit loss in kW; u shape S % represents percent voltage; delta P 0 Indicating no-load loss, with the unit of kW; I.C. A S % indicates percent no-load current;
the line impedance is updated and calculated, and the calculation formula is as follows:
Figure 702395DEST_PATH_IMAGE009
,
Figure 901295DEST_PATH_IMAGE010
,
Figure 668044DEST_PATH_IMAGE011
,
Figure 395828DEST_PATH_IMAGE012
，R L representing the equivalent resistance of the line in units of omega, X L Represents the equivalent reactance of the line, and the unit is omega, S represents the external cross-sectional area of the wire, and the unit is mm 2 And ρ is the resistivity of the conductor in Ω. mm 2 N represents the number of splits, D ge represents the geometric mean spacing of the wires in mm, r eq Representing the equivalent radius of the split conductor in mm, G L Representing the equivalent conductance of the line in units S, B L The equivalent susceptance of the line is expressed in units of S, L represents the length of the line, and the unit is km.
8. The distributed photovoltaic access prediction method of claim 1, characterized in that: the step of comparing the active power, the reactive power and the voltage of each device with preset values at each moment to determine the devices with abnormal operation comprises the following steps:
representing the abnormal statistics of time of day, comprising: the system comprises a time division line total active power, a time division line total reactive power, a time division line load rate, whether a time division line is overloaded or not, a time division distribution transformation total active power, a time division distribution transformation total reactive power, a time division distribution transformation current, a time division distribution transformation voltage, a time division distribution transformation load rate, whether a time division distribution transformation is overvoltage or not, whether a time division distribution transformation is overloaded or not, whether a time division user total active power, a time division user total reactive power, a time division user current, a time division user voltage and whether a time division user is overvoltage or not;
representative day-wide anomaly statistics, including: the total active power of the all-day line, the total reactive power of the all-day line, the daily average line load rate, the number of heavy load hours of the all-day line, the total active power of the all-day distribution transformer, the total reactive power of the all-day distribution transformer, the number of upper limit hours of the all-day distribution transformer voltage, the number of heavy load hours of the all-day distribution transformer, the comprehensive problem of the all-day distribution transformer, the total active power of the all-day user, the total reactive power of the all-day user and the number of upper limit hours of the all-day user voltage.
9. A distributed photovoltaic access prediction system, characterized in that: the method comprises the following steps:
the acquisition module is used for acquiring photovoltaic station data, power load data, power grid equipment data and meteorological related data;
the photovoltaic station data comprises active power, reactive power, daily generated energy, capacity of a single photovoltaic power station, photovoltaic output efficiency coefficient and power factor of the photovoltaic station inverter side;
the power grid equipment data comprise distribution transformer active power, distribution transformer reactive power, transformer no-load current percentage, transformer no-load loss, transformer capacity, transformer voltage, transformer short-circuit voltage percentage, transformer short-circuit loss, outer section area of a conductor, line length, conductor resistivity, conductor splitting number, conductor geometric uniform distance and split conductor equivalent radius;
the power load data comprises user active power, user reactive power and user daily electricity consumption;
the weather related data comprises temperature, humidity, solar daily radiant quantity, radiation illuminance under standard conditions and actual radiation illuminance;
the representative day determining module is used for determining a representative day according to the photovoltaic station data, the power load data and the weather related data;
the representative day data calculation module is used for calculating the load at each moment of the representative day and the photovoltaic output at each moment of the representative day;
the updating calculation module is used for updating the equivalent impedance data of the equipment according to the power grid equipment data;
the power flow calculation module is used for carrying out power flow calculation according to the active power, the reactive power, the load at each time of the representative day, the photovoltaic output at each time of the representative day and the equivalent impedance data of the equipment to obtain the active power, the reactive power and the voltage of each equipment at each time;
and the abnormity judgment module is used for comparing the active power, the reactive power and the voltage of each device at each moment with preset values to determine the devices with abnormal operation.
10. A distributed photovoltaic access prediction device, characterized in that: comprising a processor and a memory, said memory storing a computer program, said processor implementing the steps of a distributed photovoltaic access prediction method according to claim 1 when executing the computer program.
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CN112039076A (en) * 2020-05-21 2020-12-04 国网电力科学研究院有限公司 Power distribution network load flow dynamic equivalence method and system integrating distributed power sources and loads
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