CN109904865A - High-voltage distribution network peak load balance intelligence control main system - Google Patents
High-voltage distribution network peak load balance intelligence control main system Download PDFInfo
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Abstract
The invention discloses a kind of high-voltage distribution network peak loads to balance intelligence control main system, its the first pole peak regulation carried out by low-voltage platform area peak load balance intelligence control subsystem and the second pole peak regulation by itself carrying out carry out balancing the load adjustment, peak load shifting is realized by energy storage compensation and photovoltaic compensation, so that supply side determines the operation quantity of generating set according to the platform area supply load demand and power grid supply load demand in each area, reduce the operation quantity of generating set, save power supply cost, and peak regulation can be fluctuated and be limited within ± 5%, realize the electric power even running of load curve near linear, prevent generating set from dallying, it ensure that power grid security, it is economical, even running.
Description
Technical field
The present invention relates to platform area supply intelligent technology, especially a kind of high-voltage distribution network peak load balance intelligence control master
System.
Background technique
Electricity needs is there are diversity and uncertainty, and peak-valley difference is close to 50%, so that setting by client's greatest requirements are met
The hair power supply capacity set largely is idle in demand low-valley interval, increases the investment of generating set, increase investment waste,
Efficiency is reduced, electricity price cost is improved, at the same time, increases energy consumption, has discharged more pollutants, does not meet section
The requirement of energy emission reduction.
The problem of in face of this objective reality, China takes many kinds of measures:
1) summer time, the so-called summer time is the system of regulation local time artificial to be energy saving a kind of, generally in day
Bright early summer artificially by time advance one hour, can make one to get up early and go to bed early, and exposure be reduced, to make full use of illumination to provide
Source, thus the electricity consumption that saves lighting, China has carried out 6 years summer time in China between 1986 to 1991, but passes through
Statistics calculates, and the summer time simultaneously has not been changed peak-valley difference, the time advance for only peak valley occur, and the summer time can also upset people
Normal biological clock and society normal operation；
2) peak regulation supply side, reality in be mostly hydroenergy storage station, hydroenergy storage station utilize electric load low ebb when
Electric energy draws water to upper storage reservoir, is discharged water again in electric load peak period to the power station of lower storage reservoir power generation, it can be low by network load
When extra electric energy be changed into the high value electric energy of power grid peak time, be further adapted for frequency modulation, phase modulation, the cycle of stable power system
And voltage, and preferably emergency duty, it also can be improved the efficiency of thermal power station and nuclear power station in system, but hydroenergy storage station is built
If the period is long, site requirements is high, is difficult to be widely applied, and hydroenergy storage station will cause greatly during energy is converted
The energy loss of amount；
3) time-of-use tariffs management measure, time-of-use tariffs calculate separately the electricity charge, time-of-use tariffs by Peak power use and valley power consumption
System can give full play to the economic leverage of price, transfer the enthusiasm of user's peak load shifting, balanced electricity consumption, improve power grid
Rate of load condensate and utilization rate of equipment and installations, achieve the purpose that control peak load, make full use of power grid low ebb electricity, while also reached at
The purpose of this Cost Allocation, although major part provinces and cities start reality to resident's step price system in China since in July, 2012
Row, but time-of-use tariffs management can not solve the problems, such as exist from source.
Various limitations as existing for above-mentioned measure, the peak modulation capacity in the whole nation is only capable of reaching 1.7% at present, far can not
Meet actual demand, the peak load balanced capacity for how enhancing power grid becomes current urgent problem.
Summary of the invention
For technical problem present in background technique, the present invention proposes a kind of high-voltage distribution network peak load balance intelligence pipe
Control main system, which is characterized in that the high-voltage distribution network peak load balance intelligence control main system includes power grid photovoltaic power generation dress
It sets, power grid accumulation of energy energy storage device, power grid intelligent control device, multiple power grid detection units and multiple low-voltage platform area peak loads are put down
Weighing apparatus intelligence control subsystem；
Power grid photovoltaic power generation apparatus, power grid accumulation of energy energy storage device and multiple power grid detection units are separately connected power grid and intelligently manage
Control device；
Power grid intelligent control device is obtained using the platform area electricity consumption curve of prediction and the power grid photovoltaic power generation average value of prediction
Power grid supply load demand, the power grid supply load demand are lower than the peak-peak of user power utilization.
Further, the power grid photovoltaic power generation apparatus includes multiple photovoltaic generation units, for mentioning in peak of power consumption
For compensation.
Further, the photovoltaic generation unit is medium scale photo-voltaic power generation station.
Further, the power grid accumulation of energy energy storage device includes multiple groups power capacitor batteries, can be in low power consumption
Electric energy is absorbed, provides compensation in peak of power consumption.
Further, the power grid detection unit is set to the low-voltage platform area peak load balance intelligence control subsystem
System, is transferred to the power grid intelligent control device for the power information in each area in real time.
Further, the power grid intelligent control device includes central processing unit, platform area electricity consumption curve prediction unit, electricity
Net photovoltaic power generation predicting unit, electricity grid network information acquisition unit, power grid power supply parameter computing unit, the storage of operation of power networks parameter
Unit, grid balance effects analysis unit, power grid power supply information memory cell and platform area electricity consumption trend prediction unit.
Further, described area's electricity consumption curve prediction unit includes platform area electricity consumption curve prediction model, and the electricity consumption of platform area is bent
Line prediction model uses LSTM neural network, based on the historical data stored in power grid power supply information memory cell, training
Initial platform area electricity consumption curve prediction model is obtained, described area's electricity consumption curve prediction unit is instructed based on deep learning frame Keras
Practice and improve described area's electricity consumption curve prediction model.
Further, the power generation before the power grid photovoltaic power generation predicting unit collection photovoltaics generator unit within 24 hours
Information and Weather information are measured, the weather forecast within 24 hours futures is obtained by the electricity grid network information acquisition unit and is believed
Breath predicts that the power generation within the photovoltaic generation unit is 24 hours following is bent in conjunction with the operation characteristic of the photovoltaic generation unit
Line.
Further, described area's electricity consumption trend prediction unit is used for the data of each power grid detection unit of real-time monitoring,
The power demand of the low-voltage platform area peak load balance intelligence control subsystem is predicted based on big data Predicting Technique, decision is
No enabling accumulation of energy energy storage device.
Further, the power grid power supply parameter computing unit is supplied for calculating power grid supply load demand, the power grid
The specific calculating process of electrical load requirement are as follows:
1) the platform area electricity consumption curve that prediction is obtained by described area's electricity consumption curve prediction unit is used using the platform area of prediction
Electric curve calculates the platform area electricity consumption average value of prediction；
2) the power generation song within 24 hours futures of photovoltaic generation unit is obtained by the power grid photovoltaic power generation predicting unit
Line calculates the power grid photovoltaic power generation average value of prediction using the power generation curve of prediction；
3) power supply of platform area can be obtained with the power grid photovoltaic power generation average value that the platform area electricity consumption average value of prediction subtracts prediction
Workload demand.
Further, the grid balance effects analysis unit is substantially carried out three work: 1) calculating peak load regulation network fluctuation
Parameter；2) power grid accumulation of energy energy storage device utilization rate parameter is calculated；3) current high-voltage distribution network peak is evaluated based on big data association analysis
The operational effect of paddy load balance intelligence control main system.
Further, the peak load regulation network fluctuation parameters include the positive fluctuation of power grid maximum and power grid minimum negative variation, specifically
Calculation formula is as follows:
Wherein FmaxDRepresent power grid maximum positive fluctuation, FminDRepresent power grid minimum negative variation, d1、d2、…、dnRepresent power grid confession
The actual power amount stored in power information storage unit, dsDRepresent power grid supply load demand.
Further, the power grid accumulation of energy energy storage device utilization rate parameter includes power grid energy storage utilization rate and peak load regulation network benefit
With rate, specific formula for calculation is as follows:
Wherein CsaveDRepresent power grid energy storage utilization rate, CleaveDRepresent peak load regulation network utilization rate, EvDRepresent low ebb electricity price knot
Electric energy in that moment power grid accumulation of energy energy storage device of beam, EmaxDThe electric energy total capacity in power grid accumulation of energy energy storage device is represented,
EleaveDRepresent remaining electric energy in that moment power grid accumulation of energy energy storage device that low ebb electricity price starts.
Further, the power grid intelligent control device is based on cloud platform can be increased rapidly using the flexibility of cloud platform
Add calculation resources and storage resource.
Further, the high-voltage distribution network peak load balance intelligence control main system carries out the operation of two-stage peak regulation, by institute
It states low-voltage platform area peak load balance intelligence control subsystem and carries out first order peak regulation, balanced by the high-voltage distribution network peak load
Intelligence control main system carries out second level peak regulation.
Detailed description of the invention
Fig. 1 is low-voltage platform area peak load balance intelligence control subsystem structure schematic diagram；
Fig. 2 is that low-voltage platform area peak load balances intelligent management-control method flow chart；
Fig. 3 is high-voltage distribution network peak load balance intelligence control main system structural schematic diagram；
Fig. 4 is that high-voltage distribution network peak load balances intelligent management-control method flow chart.
Specific embodiment
For a clearer understanding of the technical characteristics, objects and effects of the present invention, this hair of Detailed description of the invention is now compareed
Bright specific embodiment.
In nature, wetland is the intersection in land and waters, and water level is approaching or at ground surface, or has shallow-layer ponding
Region, the specific position of wetland makes wetland as the spongy layer of nature, in the wet season, wetland energy water storage, in low water
Phase itself can then supply water, and maintain the balance of environment entirety water, use for reference wetland effect in nature, and the present invention proposes power train
" the wetland effect " of system, during referring specifically to power scheduling, when electricity is abundant, extra electricity is quickly even to " electric power wetland
Multiple " the electric power storage areas " of effect system ", collection are put at face, distributed electric power storage in blocks by face, when needing to dispatch a large amount of electric power, again
Peak power defect, the storage of whole process and natural wetland can be replenished in time from the route quick collecting in each miniature " electric power storage area "
Water process is similar.
Based on the concept of electric power wetland effect system, the present invention provides a kind of high-voltage distribution network peak load balance intelligence control
Main system, high-voltage distribution network peak load balance intelligence control main system carries out the operation of two-stage peak regulation, by high-voltage distribution network peak load
Low-voltage platform area peak load balance intelligence control subsystem inside balance intelligence control main system carries out first order peak regulation, by height
It is press-fitted net peak load balance intelligence control main system and carries out second level peak regulation.
By attached drawing 1 as can be seen that peak load balance intelligence control subsystem in low-voltage platform area includes platform area photovoltaic power generation
Device, platform area accumulation of energy energy storage device, platform area intelligent control device and multiple area's detection units.
Platform area photovoltaic power generation apparatus includes multiple photovoltaic generation units, for providing compensation in peak of power consumption, in order to most
The positions such as roof, the plant area vacant lot of illumination abundance can be arranged in photovoltaic generation unit by limits land productivity luminous energy, formed more
A micro-capacitance sensor carries out electric energy exchange by route.
Accumulation of energy energy storage device in platform area includes multiple groups self-healing rechargeable battery, specific to select NiCo/Zn rechargeable battery,
NiCo/Zn rechargeable battery using can self-regeneration hydrogel electrolyte, comprising being crosslinked by iron ion (Fe3+) in electrolyte
Sodium Polyacrylate (PANa), Sodium Polyacrylate (PANa) and NiCo/Zn rechargeable battery intermediate ion migration needed for freely move
Dynamic Zn2+ and OH-has the compatibility of height, uses Fe3+ as crosslinking agent and reinforce the healing properties of PANa hydrogel, can
The hydrogel electrolyte of self-regeneration makes
NiCo/Zn rechargeable battery is easily assembled, and has high capacity and unprecedented intrinsic self-healing ability.
Platform area detection unit is arranged at power load end, and the power information of user is transferred to platform area in real time and is intelligently managed
Device.
Platform area intelligent control device includes that central processing unit, user power utilization curve prediction unit, platform area photovoltaic power generation are pre-
Survey unit, platform area network information acquiring unit, platform area power supply parameter computing unit, platform area operating parameter storage unit, platform Qu Ping
Weigh effects analysis unit, platform area power supply information memory cell, user power utilization trend prediction unit and three-phrase burden balance unit, uses
Electricity consumption curve prediction unit in family includes user power utilization curve prediction model, and user power utilization curve prediction model uses neural network knot
Structure, input parameter is temperature, season, date, festivals or holidays, the quantity of each class factory, permanent resident population's amount, based on platform area for telecommunications
The historical data stored in breath storage unit, training obtain initial user electricity consumption curve prediction model, are collecting new confession
After power information, initial user electricity consumption curve prediction model can constantly be corrected, it is contemplated that LSTM neural network is a kind of
Time recurrent neural network is suitable for being spaced in processing and predicted time sequence and postponing relatively long critical event,
There are a variety of applications, such as predictive disease, clicking rate and stock etc. in sciemtifec and technical sphere, user power utilization curve prediction model is specifically adopted
With LSTM neural network, user power utilization curve prediction unit is based on deep learning frame Keras training and improves user power utilization song
Line prediction model, generated energy information and day before platform area photovoltaic power generation predicting unit collection photovoltaics generator unit within 24 hours
Gas information obtains the weather forecast information within 24 hours futures by platform area network information acquiring unit, in conjunction with photovoltaic power generation
The operation characteristic of unit, the power generation curve within reasonable prediction photovoltaic generation unit is 24 hours following, the platform area network information obtain
Relevant information needed for unit is used to obtain the operation of platform area by network, platform area power information memory cell for storing for each
The power supply volume information at a customer charge end, user power utilization trend prediction unit are used for the number of each area's detection unit of real-time monitoring
According to, based on big data Predicting Technique prediction customer charge end power demand, decide whether enable accumulation of energy energy storage device, centre
Reason unit is used to control the normal operation of whole system, and platform area power supply parameter computing unit is needed for calculating platform area supply load
Ask, supply side can determine the operation quantity of generating set according to platform area supply load demand, platform area supply load demand it is specific
Calculating process are as follows: 1) the user power utilization curve that prediction is obtained by user power utilization curve prediction unit is used using the user of prediction
Electric curve calculates prediction user power utilization average value；2) photovoltaic generation unit future 24 is obtained by platform area photovoltaic power generation predicting unit
Power generation curve within hour calculates pre- scaffold tower area photovoltaic power generation average value using the power generation curve of prediction；3) with prediction user
Electricity consumption average value, which subtracts pre- scaffold tower area photovoltaic power generation average value, can obtain platform area supply load demand, the storage of platform area operating parameter
Unit is for storing platform area peak regulation fluctuation parameters and platform area accumulation of energy energy storage device utilization rate parameter, platform area counterbalance effect analytical unit
It is substantially carried out three work: 1) calculating platform area peak regulation fluctuation parameters, specific calculation formula is
, wherein FmaxTRepresent the maximum positive fluctuation of platform area, FminTRepresent platform area minimum negative variation, d1、d2、…、dnRepresent platform area
The actual power amount stored in power supply information memory cell, dsTRepresent platform area supply load demand；2) the accumulation of energy energy storage of platform area is calculated
Utilization ratio of device parameter, specific calculation formula are as follows: Wherein CsaveTGeneration
The area Biao Tai energy storage utilization rate, CleaveTRepresent platform area peak regulation utilization rate, EvTRepresent that moment platform area's accumulation of energy that low ebb electricity price terminates
Electric energy in energy storage device, EmaxTRepresent the electric energy total capacity in platform area accumulation of energy energy storage device, EleaveTLow ebb electricity price is represented to start
That moment platform area's accumulation of energy energy storage device in remaining electric energy；3) particular point in time (such as the end of the month, season last
It, it is year-end), extract the relevant parameter stored in platform area operating parameter storage unit, evaluated using big data association analysis current
The operational effect of low-voltage platform area peak load balance intelligence control subsystem, provides relevant adjustment and suggests, for example whether needing
The electric energy total capacity of platform area accumulation of energy energy storage device is adjusted, three-phrase burden balance unit is low by external impressed current mutual inductor real-time detection
It presents a theatrical performance as the last item on a programme the electric current in area, current information is sent to internal controller and carries out processing analysis, to judge whether low-voltage platform area is in not
Equilibrium state, while the current value converted needed for each phase when reaching equilibrium state is calculated, then send a signal to inside
IGBT simultaneously drives its movement, by out-of-balance current from electric current it is big be mutually transferred to the small phase of electric current, finally reach three-phase equilibrium shape
State, three-phrase burden balance unit detect the variation of each phase current in low-voltage platform area automatically, compensation electricity needed for calculating and issuing in real time
Stream, so that low-voltage platform area is rapidly reached equilibrium state.Platform area intelligent control device is based on Linux server, is added using multiple GPU
The speed of fast deep learning.
Peak load balance intelligence control subsystem in low-voltage platform area carries out peak load shifting by energy storage compensation and photovoltaic compensation,
So that supply side need not determine the operation quantity of generating set according to the peak-peak of user power utilization, need to only power according to platform area
Workload demand determines the operation quantity of generating set, reduces the operation quantity of generating set, saves power supply cost, and
Platform area peak regulation can be fluctuated and be limited within ± 10%, realized the electric power even running of load curve near linear, prevent from sending out
The idle running of motor group, ensure that power grid security, economy, even running.
By attached drawing 2 as can be seen that balancing the low-voltage platform area peak of intelligence control subsystem based on low-voltage platform area peak load
Paddy load balances intelligent management-control method and specifically includes:
1) that moment (such as when 23) started in low ebb electricity price, correcting user electricity consumption curve prediction model, Jiang Taiqu
It is predicted before within nearest 24 hours of power supply information memory cell storage for power information and user power utilization curve prediction model
User power utilization curve compare, calculate user power utilization relative error, the calculation formula of the user power utilization relative error is such as
Under:
ErTRepresent user power utilization relative error, d1、d2、…、dnRepresent the reality stored in platform area power supply information memory cell
Power supply volume, d1’、d2’、…、dn' represent the prediction electricity consumption that the moment is corresponded in user power utilization prediction curve；
If user power utilization relative error be less than or equal to first threshold (such as 15%), need not correcting user electricity consumption curve it is pre-
Model is surveyed, if error is greater than first threshold, is less than or equal to second threshold (such as 25%), is then based on newest power supply information data
Correcting user electricity consumption curve prediction model judges whether are real air temperature and weather forecast temperature if error is greater than second threshold
Have larger difference, if there is larger difference, illustrate the parameter for inputting user power utilization curve prediction model differed with actual value compared with
Greatly, it is not necessary to correcting user electricity consumption curve prediction model, conversely, then correcting user electricity consumption curve prediction model；
2) by parameters such as the temperature of weather forecast, season, date, festivals or holidays, the quantity of each class factory, permanent resident population's amounts
User power utilization curve prediction model is inputted, obtains the user power utilization curve of following 24 hours interior predictions, the prediction of platform area photovoltaic power generation
Unit predicts that the power generation curve in 24 hours futures of photovoltaic generation unit, platform area power supply parameter computing unit utilize the user predicted
Electricity consumption curve and the platform area photovoltaic power generation curve of prediction calculate platform area supply load demand, power supply unit reference station area supply load
Demand provides electric energy；
3) during low ebb electricity price (such as 23 when next day 7), low-price electricity is stored in the accumulation of energy of platform area by central processing unit
Energy storage device；
4) pass through platform area photovoltaic power generation apparatus for multiple photovoltaics at that moment (such as when next day 7) that low ebb electricity price terminates
The electric energy of generator unit inputs major network distribution system；
5) data of each area's detection unit of user power utilization trend prediction unit real-time monitoring predict skill based on big data
Art predicts the power demand at customer charge end, (such as 30 divides with 18 up to 23 when 30 divide to 11 when 8 in two peak of power consumption periods
When), if prediction electricity consumption is greater than current power supply capacity, central processing unit is sent a signal to, central processing unit opens platform
Accumulation of energy energy storage device in area's provides power compensation by platform area accumulation of energy energy storage device, in the off-peak period on daytime, if prediction electricity consumption
Amount is less than current power supply capacity, sends a signal to central processing unit, and extra electric energy is stored in platform area by central processing unit
Accumulation of energy energy storage device；
6) each use is directed within 24 hours before particular point in time (such as when next day 23), central processing unit statistics
The electric power thus supplied at family, is deposited into platform area power supply information memory cell, and platform area tune is calculated in platform area counterbalance effect analytical unit
Peak fluctuation parameters and platform area accumulation of energy energy storage device utilization rate parameter, are deposited into platform area operating parameter storage unit.
It is balanced in intelligence control subsystem operational process in low-voltage platform area peak load, three in platform area intelligent control device
Phase load balancing unit is constantly in working condition, guarantees the three-phrase burden balance of low-voltage platform area, reduces the electric energy loss of route,
Guarantee the safe operation of electrical equipment.
By attached drawing 3 as can be seen that high-voltage distribution network peak load balance intelligence control main system includes power grid photovoltaic power generation
Device, power grid accumulation of energy energy storage device, power grid intelligent control device, multiple power grid detection units and multiple low-voltage platform area peak loads
Balance intelligence control subsystem.
Power grid photovoltaic power generation apparatus includes multiple photovoltaic generation units, for providing compensation in peak of power consumption, it is contemplated that
The compensation electricity consumption at power grid end is larger, and photovoltaic generation unit is medium scale photo-voltaic power generation station.
Power grid accumulation of energy energy storage device includes multiple groups power capacitor batteries, can absorb electric energy in low power consumption, with
Compensation is provided when electric peak, due to using power capacitor batteries, accumulation of energy energy storage device has three advantages: 1) using longevity number
Long, the longevity number of the general battery of the cycle ratio of power battery is long very much, will not because of battery longevity number reason and influence accumulation of energy
The operating of energy storage device；2) maintenance cost is low, and since the cycle ratio of power capacitor batteries is more, accumulation of energy energy storage device will not
Benefit is influenced because of its cell decay；3) highly-safe, power capacitor batteries are because the material of itself determines that it will not send out
Make to burn explosion, so that big destructive accident will not occur for accumulation of energy energy storage device, just because of above-mentioned advantage, accumulation of energy energy storage dress
It sets and can satisfy the requirement that high-voltage distribution network peak load balance intelligence control main system stablizes long-term safety low cost operation.
Power grid detection unit is set to low-voltage platform area peak load balance intelligence control subsystem, in real time by each area
Power information be transferred to power grid intelligent control device.
Power grid intelligent control device includes that central processing unit, platform area electricity consumption curve prediction unit, power grid photovoltaic power generation are pre-
It is flat to survey unit, electricity grid network information acquisition unit, power grid power supply parameter computing unit, operation of power networks parameter storage unit, power grid
Weigh effects analysis unit, power grid power supply information memory cell and platform area electricity consumption trend prediction unit, platform area electricity consumption curve prediction list
Member includes platform area electricity consumption curve prediction model, and electricity consumption curve prediction model in platform area uses neural network structure, and input parameter is temperature
Degree, season, date, festivals or holidays, the quantity of each class factory, permanent resident population's amount, based on power grid power supply information memory cell in deposit
The historical data of storage, training obtain initial platform area electricity consumption curve prediction model, collect it is new for power information after, can be right
Initial platform area electricity consumption curve prediction model is constantly corrected, and platform area electricity consumption curve prediction model still uses LSTM nerve net
Network, platform area electricity consumption curve prediction unit are based on deep learning frame Keras training and improve platform area electricity consumption curve prediction model, electricity
Generated energy information and Weather information before net photovoltaic power generation predicting unit collection photovoltaics generator unit within 24 hours, pass through electricity
Net network information acquiring unit obtains the weather forecast information within 24 hours futures, and the operation in conjunction with photovoltaic generation unit is special
Property, the power generation curve within reasonable prediction photovoltaic generation unit is 24 hours following, electricity grid network information acquisition unit is for passing through
Relevant information needed for network obtains operation of power networks, power grid power information memory cell for storing for each low-voltage platform area peak
The power supply volume information of paddy load balance intelligence control subsystem, platform area electricity consumption trend prediction unit are used for each power grid of real-time monitoring
The data of detection unit, the electricity consumption based on big data Predicting Technique prediction low-voltage platform area peak load balance intelligence control subsystem
Demand, decides whether enabling power grid accumulation of energy energy storage device, and central processing unit is used to control the normal operation of whole system, power grid
Power supply parameter computing unit can be determined according to power grid supply load demand and be generated electricity for calculating power grid supply load demand, supply side
The operation quantity of unit, the specific calculating process of power grid supply load demand are as follows: 1) obtained by platform area electricity consumption curve prediction unit
The platform area electricity consumption curve that must be predicted calculates pre- scaffold tower area electricity consumption average value using the platform area electricity consumption curve of prediction；2) pass through power grid
Photovoltaic power generation predicting unit obtains the power generation curve within 24 hours futures of photovoltaic generation unit, utilizes the power generation curve meter of prediction
Calculate prediction power grid photovoltaic power generation average value；3) prediction power grid photovoltaic power generation average value is subtracted with pre- scaffold tower area electricity consumption average value
Power grid supply load demand is obtained, operation of power networks parameter storage unit is for storing peak load regulation network fluctuation parameters and power grid accumulation of energy storage
Energy utilization ratio of device parameter, grid balance effects analysis unit are substantially carried out three work: 1) peak load regulation network fluctuation parameters are calculated,
Specifically calculation formula is
, wherein FmaxDRepresent power grid maximum positive fluctuation, FminDRepresent power grid minimum negative variation, d1、d2、…、dnRepresent power grid
The actual power amount stored in power supply information memory cell, dsDRepresent power grid supply load demand；2) power grid accumulation of energy energy storage is calculated
Utilization ratio of device parameter, specific calculation formula are as follows:Wherein CsaveD
Represent power grid energy storage utilization rate, CleaveDRepresent peak load regulation network utilization rate, EvDThat moment power grid that low ebb electricity price terminates is represented to store
Electric energy in energy energy storage device, EmaxDRepresent the electric energy total capacity in power grid accumulation of energy energy storage device, EleaveDLow ebb electricity price is represented to open
Remaining electric energy in that the moment power grid accumulation of energy energy storage device to begin；3) particular point in time (such as the end of the month, season last
It, it is year-end), extract the relevant parameter stored in operation of power networks parameter storage unit, evaluated using big data association analysis current
The operational effect of high-voltage distribution network peak load balance intelligence control main system, provides relevant adjustment and suggests, for example whether needing
Adjust the electric energy total capacity of power grid accumulation of energy energy storage device.Power grid intelligent control device is based on cloud platform, utilizes the flexible of cloud platform
Property, the calculation resources that can increase sharply and storage resource.
High-voltage distribution network peak load balance intelligence control main system passes through low-voltage platform area peak load balance intelligence control
The first order peak regulation and the second level peak regulation by itself carrying out that system carries out carry out balancing the load adjustment, by energy storage compensation and light
Peak load shifting is realized in volt compensation, so that supply side need not determine the operation number of generating set according to the peak-peak of user power utilization
Amount only need to determine the operation number of generating set according to the platform area supply load demand and power grid supply load demand in each area
Amount, reduces the operation quantity of generating set, saves power supply cost, and can fluctuate peak regulation and be limited within ± 5%,
The electric power even running for realizing load curve near linear, prevents generating set from dallying, and ensure that power grid security, economy, steady
Operation.
By attached drawing 4 as can be seen that balancing the high-voltage distribution network peak of intelligence control main system based on high-voltage distribution network peak load
Paddy load balances intelligent management-control method and specifically includes:
1) intelligence is balanced by the low-voltage platform area peak load that high-voltage distribution network peak load balances inside intelligence control main system
It manages subsystem and carries out first order peak regulation；
2) intelligence control main system is balanced by high-voltage distribution network peak load and carries out second level peak regulation, second level peak regulation specifically wraps
Include following steps:
(1) that moment (such as when 23) started in low ebb electricity price, correction station area electricity consumption curve prediction model, by power grid
It is predicted before within nearest 24 hours of power supply information memory cell storage for power information and platform area electricity consumption curve prediction model
Platform area electricity consumption curve compare, calculate platform area electricity consumption relative error, the calculation formula of described area's electricity consumption relative error is such as
Under:
ErDRepresent platform area electricity consumption relative error, d1、d2、…、dnRepresent the reality stored in power grid power supply information memory cell
Power supply volume, d1’、d2’、…、dn ’Represent the prediction electricity consumption that the moment is corresponded in platform area power consumption prediction curve；
The area Ruo Tai electricity consumption relative error be less than or equal to first threshold (such as 15%), then need not correction station area electricity consumption curve it is pre-
Model is surveyed, if error is greater than first threshold, is less than or equal to second threshold (such as 25%), is then based on newest power supply information data
Electricity consumption curve prediction model in correction station area judges whether are real air temperature and weather forecast temperature if error is greater than second threshold
Have larger difference, if there is larger difference, illustrate the parameter of input table area electricity consumption curve prediction model differed with actual value compared with
Greatly, it is not necessary to correction station area electricity consumption curve prediction model, conversely, then correction station area electricity consumption curve prediction model；
(2) temperature of weather forecast, season, date, festivals or holidays, the quantity of each class factory, permanent resident population's amount etc. are joined
Number input table area electricity consumption curve prediction model, obtains the platform area electricity consumption curve of following 24 hours interior predictions, and power grid photovoltaic power generation is pre-
The power generation curve in unit prediction 24 hours futures of photovoltaic generation unit is surveyed, power grid power supply parameter computing unit utilizes the platform predicted
Area's electricity consumption curve and the power grid photovoltaic power generation curve of prediction calculate power grid supply load demand, and power supply unit is negative with reference to power grid power supply
Lotus demand provides electric energy；
(3) during low ebb electricity price (such as 23 when next day 7), low-price electricity is stored in power grid accumulation of energy by central processing unit
Energy storage device；
(4) pass through power grid photovoltaic power generation apparatus for multiple light at that moment (such as when next day 7) that low ebb electricity price terminates
The electric energy for lying prostrate generator unit inputs major network distribution system；
(5) data of each power grid detection unit of platform area electricity consumption trend prediction unit real-time monitoring are predicted based on big data
The power demand of technological prediction low-voltage platform area peak load balance intelligence control subsystem, in two peak of power consumption periods (such as 8
When 30 30 divide with 18 up to 23 when dividing to 11), if prediction electricity consumption is greater than current power supply capacity, send a signal to centre
Unit is managed, central processing unit opens power grid accumulation of energy energy storage device, power compensation is provided by power grid accumulation of energy energy storage device, white
It off-peak period sends a signal to central processing unit, central processing if prediction electricity consumption is less than current power supply capacity
Extra electric energy is stored in power grid accumulation of energy energy storage device by unit；
(6) it is directed within 24 hours before particular point in time (such as when next day 23), central processing unit statistics each
The electric power thus supplied of low-voltage platform area peak load balance intelligence control subsystem, is deposited into power grid power supply information memory cell, electricity
Peak load regulation network fluctuation parameters and power grid accumulation of energy energy storage device utilization rate parameter are calculated in net counterbalance effect analytical unit, are deposited
Enter operation of power networks parameter storage unit.
First order peak regulation and second level peak regulation carry out simultaneously, and sequencing is not present.
The above disclosure is only the preferred embodiments of the present invention, cannot limit the right model of the present invention with this certainly
It encloses, therefore equivalent changes made in accordance with the claims of the present invention, is still within the scope of the present invention.
Claims (11)
1. a kind of high-voltage distribution network peak load balance intelligence control main system, which is characterized in that the high-voltage distribution network peak load
Balance intelligence control main system includes power grid photovoltaic power generation apparatus, power grid accumulation of energy energy storage device, power grid intelligent control device, multiple
Power grid detection unit and multiple low-voltage platform area peak loads balance intelligence control subsystem；
Power grid photovoltaic power generation apparatus, power grid accumulation of energy energy storage device and multiple power grid detection units are separately connected power grid intelligently control dress
It sets；
Power grid intelligent control device obtains power grid using the platform area electricity consumption curve of prediction and the power grid photovoltaic power generation average value of prediction
Supply load demand, the power grid supply load demand are lower than the peak-peak of user power utilization.
2. high-voltage distribution network peak load balance intelligence control main system according to claim 1, which is characterized in that the electricity
Net photovoltaic power generation apparatus includes multiple photovoltaic generation units, for providing compensation in peak of power consumption；The photovoltaic generation unit
For medium scale photo-voltaic power generation station；The power grid accumulation of energy energy storage device includes multiple groups power capacitor batteries, can be in electricity consumption
Electric energy is absorbed when low ebb, provides compensation in peak of power consumption；It is negative that the power grid detection unit is set to the low-voltage platform area peak valley
Lotus balance intelligence control subsystem, is transferred to the power grid intelligent control device for the power information in each area in real time.
3. high-voltage distribution network peak load balance intelligence control main system according to claim 1, which is characterized in that the electricity
Net intelligent control device includes central processing unit, platform area electricity consumption curve prediction unit, power grid photovoltaic power generation predicting unit, power grid
Network information acquiring unit, power grid power supply parameter computing unit, operation of power networks parameter storage unit, grid balance effect analysis list
Member, power grid power supply information memory cell and platform area electricity consumption trend prediction unit.
4. high-voltage distribution network peak load balance intelligence control main system according to claim 3, which is characterized in that described
Electricity consumption curve prediction unit in area's includes platform area electricity consumption curve prediction model, and electricity consumption curve prediction model in platform area uses LSTM nerve net
Network, based on the historical data stored in power grid power supply information memory cell, training obtains initial platform area electricity consumption curve prediction
Model, described area's electricity consumption curve prediction unit are based on deep learning frame Keras training and improve described area's electricity consumption curve
Prediction model.
5. high-voltage distribution network peak load balance intelligence control main system according to claim 3, which is characterized in that the electricity
Generated energy information and Weather information before net photovoltaic power generation predicting unit collection photovoltaics generator unit within 24 hours, pass through institute
The weather forecast information within electricity grid network information acquisition unit 24 hours futures of acquisition is stated, in conjunction with the photovoltaic generation unit
Operation characteristic predicts the power generation curve within the photovoltaic generation unit is 24 hours following.
6. high-voltage distribution network peak load balance intelligence control main system according to claim 3, which is characterized in that described
Electricity consumption trend prediction unit in area's is used for the data of each power grid detection unit of real-time monitoring, predicts institute based on big data Predicting Technique
The power demand for stating low-voltage platform area peak load balance intelligence control subsystem, decides whether to enable accumulation of energy energy storage device.
7. high-voltage distribution network peak load balance intelligence control main system according to claim 3, which is characterized in that the electricity
Net power supply parameter computing unit is for calculating power grid supply load demand, the specific calculating process of the power grid supply load demand
Are as follows:
1) the platform area electricity consumption curve that prediction is obtained by described area's electricity consumption curve prediction unit, it is bent using the platform area electricity consumption of prediction
The platform area electricity consumption average value of line computation prediction；
2) the power generation curve within 24 hours futures of photovoltaic generation unit, benefit are obtained by the power grid photovoltaic power generation predicting unit
The power grid photovoltaic power generation average value of prediction is calculated with the power generation curve of prediction；
3) platform area supply load can be obtained with the power grid photovoltaic power generation average value that the platform area electricity consumption average value of prediction subtracts prediction
Demand.
8. high-voltage distribution network peak load balance intelligence control main system according to claim 3, which is characterized in that the electricity
Net counterbalance effect analytical unit is substantially carried out three work: 1) calculating peak load regulation network fluctuation parameters；2) power grid accumulation of energy energy storage is calculated
Utilization ratio of device parameter；3) current high-voltage distribution network peak load balance intelligence control main system is evaluated based on big data association analysis
Operational effect.
9. high-voltage distribution network peak load balance intelligence control main system according to claim 8, which is characterized in that the electricity
Net peak regulation fluctuation parameters include the positive fluctuation of power grid maximum and power grid minimum negative variation, and specific formula for calculation is as follows:
Wherein FmaxDRepresent power grid maximum positive fluctuation, FminDRepresent power grid minimum negative variation, d1、d2、…、dnPower grid is represented for telecommunications
The actual power amount stored in breath storage unit, dsDRepresent power grid supply load demand；The power grid accumulation of energy energy storage device utilizes
Rate parameter includes power grid energy storage utilization rate and peak load regulation network utilization rate, and specific formula for calculation is as follows:
Wherein CsaveDRepresent power grid energy storage utilization rate, CleaveDRepresent peak load regulation network utilization rate, EvDRepresent that low ebb electricity price terminates
Electric energy in a moment power grid accumulation of energy energy storage device, EmaxDRepresent the electric energy total capacity in power grid accumulation of energy energy storage device, EleaveDGeneration
Remaining electric energy in that moment power grid accumulation of energy energy storage device that table low ebb electricity price starts.
10. according to the described in any item high-voltage distribution network peak load balance intelligence control main systems of claim 6-13, feature
Be, the power grid intelligent control device be based on cloud platform, using the flexibility of cloud platform, the calculation resources that can increase sharply and
Storage resource.
11. high-voltage distribution network peak load balance intelligence control main system according to claim 9, which is characterized in that described
High-voltage distribution network peak load balance intelligence control main system carries out the operation of two-stage peak regulation, is balanced by the low-voltage platform area peak load
Intelligence control subsystem carries out first order peak regulation, balances intelligence control main system by the high-voltage distribution network peak load and carries out second
Grade peak regulation.
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