CN109842141A - Low-voltage platform area peak load balances intelligent management - Google Patents
Low-voltage platform area peak load balances intelligent management Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/50—Arrangements for eliminating or reducing asymmetry in polyphase networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
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Abstract
The invention discloses a kind of low-voltage platform area peak loads to balance intelligent management, peak load shifting is carried out by energy storage compensation and photovoltaic compensation in equilibrium process, so that supply side need to only determine relatively stable supply according to low-voltage platform area supply load demand, make its system stable operation, the operation quantity of unit when reducing peak, save power supply cost, and platform area peak regulation can be fluctuated and be limited within ± 10%, realize the electric load even running of near linear, it prevents generating set from dallying, ensure that power grid security, economy, even running.
Description
Technical field
The present invention relates to platform area supply intelligent technology, especially a kind of low-voltage platform area peak load balances intelligent management side
Method.
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) variable load plant, reality in be mostly hydroenergy storage station, hydroenergy storage station utilize electric load low ebb when electricity
Can draw water to upper storage reservoir, electric load peak period discharge water again to lower storage reservoir generate electricity power station, when it can be low by network load
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, also can be improved the efficiency of thermal power station and nuclear power station in system, but the construction of hydroenergy storage station
Period is long, and site requirements is high, is difficult to be widely applied, and hydroenergy storage station will cause largely during energy is converted
Energy loss；
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 low-voltage platform area peak load balance intelligence pipe
Reason method, which is characterized in that the low-voltage platform area peak load balances intelligent management and includes:
1) that moment started in low ebb electricity price, correcting user electricity consumption curve prediction model；
2) it by the user power utilization curve of the following 24 hours interior predictions of user power utilization curve prediction model prediction, predicts simultaneously
The platform area photovoltaic power generation curve of following 24 hours interior predictions, utilizes the user power utilization curve of the prediction and the platform area of the prediction
Photovoltaic power generation curve calculates platform area supply load demand, and power supply unit provides electric energy with reference to described area's supply load demand；
3) during low ebb electricity price, low-price electricity is stored in platform area accumulation of energy energy storage device；
4) electric energy of multiple photovoltaic generation units is inputted major network distribution system by that moment terminated in low ebb electricity price；
5) data of each area's detection unit of real-time monitoring predict the power demand at customer charge end, for described
Area's accumulation of energy energy storage device is controlled.
Further, in the step 1, the correcting user electricity consumption curve prediction model is specifically included:
By being compared for power information with the user power utilization curve predicted before within nearest 24 hours, calculates user and use
Electric relative error need not correct the user power utilization curve if the user power utilization relative error is less than or equal to first threshold
Prediction model is less than or equal to second threshold if the user power utilization relative error is greater than first threshold, then is based on newest power supply
Information data corrects the user power utilization curve prediction model, if the user power utilization relative error is greater than second threshold, sentences
Whether disconnected real air temperature and weather forecast temperature have larger difference, if there is larger difference, illustrate to input the user power utilization
The parameter of curve prediction model differs larger with actual value, it is not necessary to the user power utilization curve prediction model is corrected, conversely, then school
The just described user power utilization curve prediction model.
Further, the calculation formula of the user power utilization relative error is as follows:
ErTRepresent user power utilization relative error, d1、d2、…、dnRepresent actual power amount, d1 ’、d2 ’、…、dn ’Represent user
The prediction electricity consumption at moment is corresponded in power consumption prediction curve.
Further, the user power utilization curve prediction model uses LSTM neural network, is based on deep learning frame
Keras is trained and improves.
Further, in the step 2, the platform area photovoltaic power generation curve for predicting following 24 hours interior predictions is specifically wrapped
It includes:
Generated energy information and Weather information before collection photovoltaics generator unit within 24 hours, obtain following 24 hours with
Interior weather forecast information, in conjunction with the operation characteristic of photovoltaic generation unit, following 24 hours of reasonable prediction photovoltaic generation unit with
Interior power generation curve.
Further, in the step 2, the specific calculating process of described area's supply load demand includes:
1) prediction user power utilization average value is calculated using the user power utilization curve of prediction；
2) pre- scaffold tower area photovoltaic power generation average value is calculated using the platform area photovoltaic power generation curve of prediction；
3) with the prediction user power utilization average value subtract the pre- scaffold tower area photovoltaic power generation average value can obtain it is described
Platform area supply load demand.
Further, in the step 5, in two peak of power consumption periods, if prediction electricity consumption is greater than current for electric energy
Power opens described area's accumulation of energy energy storage device, power compensation is provided by described area's accumulation of energy energy storage device, in the non-height on daytime
Extra electric energy is stored in described area's accumulation of energy energy storage device if prediction electricity consumption is less than current power supply capacity by the peak period.
Further, the low-voltage platform area peak load balances intelligent management further include:
6) electric power thus supplied of each user is directed within 24 hours before particular point in time, statistics, is deposited into platform area
Power supply information memory cell, is calculated platform area peak regulation fluctuation parameters and platform area accumulation of energy energy storage device utilization rate parameter, is deposited
The area Ru Tai operating parameter storage unit.
Further, described area's peak regulation fluctuation parameters include the maximum positive fluctuation of platform area and platform area minimum negative variation, specifically
Calculation formula is as follows:
FmaxTRepresent the maximum positive fluctuation of platform area, FminTRepresent platform area minimum negative variation, d1、d2、…、dnPlatform area is represented for telecommunications
The actual power amount stored in breath storage unit, dsTRepresent platform area supply load demand.
Further, described area's accumulation of energy energy storage device utilization rate parameter includes platform area energy storage utilization rate and platform area peak regulation benefit
With rate, specific formula for calculation is as follows:
CsaveTRepresent platform area energy storage utilization rate, CleaveTRepresent platform area peak regulation utilization rate, EvTRepresent what low ebb electricity price terminated
Electric energy in that moment platform area's accumulation of energy energy storage device, EmaxTRepresent the electric energy total capacity in platform area accumulation of energy energy storage device, EleaveT
Represent remaining electric energy in that moment platform area's accumulation of energy energy storage device that low ebb electricity price starts.
Further, the low-voltage platform area peak load balance intelligent management detects low pressure automatically in the process of implementation
The variation of each phase current in platform area compensates electric current needed for calculating and issuing in real time, so that low-voltage platform area is rapidly reached equilibrium state.
Detailed description of the invention
Fig. 1 is low-voltage platform area peak load balance intelligent management subsystem structure schematic diagram；
Fig. 2 is low-voltage platform area peak load balance intelligent management flow chart；
Fig. 3 is high-voltage distribution network peak load balance intelligent management main system structural schematic diagram；
Fig. 4 is high-voltage distribution network peak load balance intelligent management 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 intelligent management
Main system, high-voltage distribution network peak load balances intelligent management main system and carries out the operation of two-stage peak regulation, by high-voltage distribution network peak load
It balances the low-voltage platform area peak load balance intelligent management subsystem inside intelligent management main system and carries out first order peak regulation, by height
It is press-fitted net peak load balance intelligent management main system and carries out second level peak regulation.
By attached drawing 1 as can be seen that it includes platform area photovoltaic power generation that low-voltage platform area peak load, which balances intelligent management subsystem,
Device, platform area accumulation of energy energy storage device, platform area intelligent management apapratus 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 is easily assembled NiCo/Zn rechargeable battery, 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 intelligent management in real time
Device.
Platform area intelligent management apapratus 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 energy storage compensating module, 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
It asks, power plant can determine the operation quantity of generating set, the specific meter of platform area supply load demand according to platform area supply load demand
Calculation process are as follows: 1) the user power utilization curve that prediction is obtained by user power utilization curve prediction unit utilizes the user power utilization of prediction
Curve calculates prediction user power utilization average value；2) small by platform area photovoltaic power generation predicting unit acquisition photovoltaic generation unit future 24
When within power generation curve, utilize the power generation curve of prediction to calculate pre- scaffold tower area photovoltaic power generation average value；3) it is used with prediction user
Level mean value, which subtracts pre- scaffold tower area photovoltaic power generation average value, can obtain platform area supply load demand, and operating parameter storage in platform area is single
Member 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 master
It carries 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 CsaveT
Represent platform area energy storage utilization rate, CleaveTRepresent platform area peak regulation utilization rate, EvTThat moment platform area that low ebb electricity price terminates is represented to store
Electric energy in energy 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 open
Remaining electric energy in that the moment platform area's 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 platform area operating parameter storage unit, evaluated using big data association analysis current
Low-voltage platform area peak load balances the operational effect of intelligent management 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 management apapratus is based on Linux server, is added using multiple GPU
The speed of fast deep learning.
Low-voltage platform area peak load balances intelligent management subsystem and carries out peak load shifting by energy storage compensation and photovoltaic compensation,
So that power plant 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 negative
Lotus demand determines the operation quantity of generating set, reduces the operation quantity of generating set, saves power supply cost, and energy
Peak regulation fluctuation in the area Gou Jiangtai is limited within ± 10%, is realized the electric power output of near linear, is prevented generating set from dallying, guarantee
The equipment safety of generating set.
By attached drawing 2 as can be seen that balancing the low-voltage platform area peak of intelligent management subsystem based on low-voltage platform area peak load
Paddy load balance intelligent management 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 intelligent management subsystem operational process in low-voltage platform area peak load, three in platform area intelligent management apapratus
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 it includes power grid photovoltaic power generation that high-voltage distribution network peak load, which balances intelligent management main system,
Device, power grid accumulation of energy energy storage device, power grid intelligent management apapratus, multiple power grid detection units and multiple low-voltage platform area peak loads
Balance intelligent management 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, energy storage compensating module 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 energy storage
The operating of compensating module；2) maintenance cost is low, and since the cycle ratio of power capacitor batteries is more, energy storage compensating module 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 energy storage compensating module, just because of above-mentioned advantage, energy storage compensates mould
Block can satisfy the requirement that high-voltage distribution network peak load balance intelligent management main system stablizes long-term safety low cost operation.
Power grid detection unit is set to low-voltage platform area peak load balance intelligent management subsystem, in real time by each area
Power information be transferred to power grid intelligent management apapratus.
Power grid intelligent management apapratus 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
Paddy load balances the power supply volume information of intelligent management subsystem, and electricity consumption trend prediction unit in platform area is 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 intelligent management 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
For power supply parameter computing unit for calculating power grid supply load demand, power plant can determine generator according to power grid supply load demand
The operation quantity of group, 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 of prediction calculates pre- scaffold tower area electricity consumption average value using the platform area electricity consumption curve of prediction；2) pass through power grid light
Volt power generation predicting unit obtains the power generation curve within 24 hours futures of photovoltaic generation unit, and the power generation curve of prediction is utilized to calculate
Predict power grid photovoltaic power generation average value；3) subtracting prediction power grid photovoltaic power generation average value with pre- scaffold tower area electricity consumption average value can obtain
To power grid supply load demand, operation of power networks parameter storage unit is for storing peak load regulation network fluctuation parameters and power grid accumulation of energy energy storage
Utilization ratio of device parameter, grid balance effects analysis unit are substantially carried out three work: 1) calculating peak load regulation network fluctuation parameters, tool
The calculation formula of body 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
High-voltage distribution network peak load balances the operational effect of intelligent management 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 management apapratus 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 balances intelligent management main system and balances intelligent management by low-voltage platform area peak load
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 power plant 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 output for realizing near linear, prevents generating set from dallying, ensure that the equipment safety of generating set.
By attached drawing 4 as can be seen that balancing the high-voltage distribution network peak of intelligent management main system based on high-voltage distribution network peak load
Paddy load balance intelligent management specifically includes:
1) intelligence is balanced by the low-voltage platform area peak load inside high-voltage distribution network peak load balance intelligent management main system
Management subsystem carries out first order peak regulation；
2) second level peak regulation is carried out by high-voltage distribution network peak load balance intelligent management main system, 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
Technological prediction low-voltage platform area peak load balances the power demand of intelligent management 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
Low-voltage platform area peak load balances the electric power thus supplied of intelligent management 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 (10)
1. a kind of low-voltage platform area peak load balances intelligent management, which is characterized in that the low-voltage platform area peak load is flat
Weighing apparatus intelligent management include:
1) start time around, correcting user electricity consumption curve prediction model in low ebb electricity price；
2) by the user power utilization curve of the following 24 hours interior predictions of user power utilization curve prediction model prediction, while future is predicted
The platform area photovoltaic power generation curve of 24 hours interior predictions utilizes the user power utilization curve of the prediction and the platform Qu Guangfu of the prediction
Generate electricity curve, calculates platform area supply load demand, and power supply unit provides electric energy with reference to described area's supply load demand；
3) during low ebb electricity price, low-price electricity is stored in platform area accumulation of energy energy storage device；
4) terminate time around in low ebb electricity price, the electric energy of multiple photovoltaic generation units is inputted into major network distribution system；
5) data of each area's detection unit of real-time monitoring predict the power demand at customer charge end, and described area is stored
Energy energy storage device is controlled.
2. low-voltage platform area peak load according to claim 1 balances intelligent management, which is characterized in that the step
In 1, the correcting user electricity consumption curve prediction model is specifically included:
By comparing for power information with the user power utilization curve predicted before for nearest fixed cycle, it is opposite to calculate user power utilization
Error need not correct the user power utilization curve prediction mould if the user power utilization relative error is less than or equal to first threshold
Type corrects the user based on newest power supply information data and uses if the user power utilization relative error is greater than first threshold
Electric curve prediction model judges real air temperature and weather forecast gas if the user power utilization relative error is greater than second threshold
Whether temperature has larger difference, if there is larger difference, illustrates the parameter and reality that input the user power utilization curve prediction model
Actual value difference is larger, it is not necessary to the user power utilization curve prediction model is corrected, conversely, then correcting the user power utilization curve prediction
Model.
3. low-voltage platform area peak load according to claim 2 balances intelligent management, which is characterized in that the user
The calculation formula of electricity consumption relative error is as follows:
ErTRepresent user power utilization relative error, d1、d2、…、dnRepresent actual power amount, d1 ’、d2 ’、…、dn ’Represent user power utilization
The prediction electricity consumption at moment is corresponded in prediction curve.
4. low-voltage platform area peak load according to claim 1 balances intelligent management, which is characterized in that the user
Electricity consumption curve prediction model uses LSTM neural network, is trained and is improved based on deep learning frame Keras.
5. low-voltage platform area peak load according to claim 1 balances intelligent management, which is characterized in that the step
In 2, the platform area photovoltaic power generation curve for predicting following 24 hours interior predictions is specifically included:
Generated energy information and Weather information before collection photovoltaics generator unit within 24 hours will obtain within 24 hours futures
Weather forecast information, in conjunction with the operation characteristic of photovoltaic generation unit, within reasonable prediction photovoltaic generation unit is 24 hours following
Generate electricity curve.
6. low-voltage platform area peak load according to claim 1 balances intelligent management, which is characterized in that the step
In 2, the specific calculating process of described area's supply load demand includes:
1) prediction user power utilization average value is calculated using the user power utilization curve of prediction；
2) pre- scaffold tower area photovoltaic power generation average value is calculated using the platform area photovoltaic power generation curve of prediction；
3) described area can be obtained by subtracting the pre- scaffold tower area photovoltaic power generation average value with the prediction user power utilization average value
Supply load demand.
7. low-voltage platform area peak load according to claim 1 balances intelligent management, which is characterized in that the step
In 5, in two peak of power consumption periods, if prediction electricity consumption is greater than current power supply capacity, described area's accumulation of energy energy storage dress is opened
It sets, provides power compensation by described area's accumulation of energy energy storage device, in the off-peak period on daytime, work as if prediction electricity consumption is less than
Extra electric energy is stored in described area's accumulation of energy energy storage device by preceding power supply capacity.
8. low-voltage platform area peak load according to claim 1 balances intelligent management, which is characterized in that the low pressure
Platform area peak load balances intelligent management further include:
6) electric power thus supplied of each user is directed within 24 hours before particular point in time, statistics, is deposited into the power supply of platform area
Information memory cell is calculated platform area peak regulation fluctuation parameters and platform area accumulation of energy energy storage device utilization rate parameter, is deposited into platform
Area's operating parameter storage unit.
9. low-voltage platform area peak load according to claim 8 balances intelligent management, which is characterized in that described area
Peak regulation fluctuation parameters include the maximum positive fluctuation of platform area and platform area minimum negative variation, and specific formula for calculation is as follows:
FmaxTRepresent the maximum positive fluctuation of platform area, FminTRepresent platform area minimum negative variation, d1、d2、…、dnPlatform area is represented to deposit for power information
The actual power amount stored in storage unit, dsTRepresent platform area supply load demand.
10. low-voltage platform area peak load according to claim 8 balances intelligent management, which is characterized in that described
Accumulation of energy energy storage device utilization rate parameter in area's includes platform area energy storage utilization rate and platform area peak regulation utilization rate, and specific formula for calculation is as follows:
CsaveTRepresent platform area energy storage utilization rate, CleaveTRepresent platform area peak regulation utilization rate, EvTWhen representing that low ebb electricity price terminates
Electric energy in the area Ke Tai accumulation of energy energy storage device, EmaxTRepresent the electric energy total capacity in platform area accumulation of energy energy storage device, EleaveTIt represents low
Remaining electric energy in that moment platform area's accumulation of energy energy storage device that paddy electricity valence starts；The low-voltage platform area peak load balance intelligence
Management method detects the variation of each phase current in low-voltage platform area automatically in the process of implementation, compensation electricity needed for calculating and issuing in real time
Stream, so that low-voltage platform area is rapidly reached equilibrium state.
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