CN110247411A - A kind of user side instrumentation intelligence light storage plant capacity dynamic allocation method - Google Patents
A kind of user side instrumentation intelligence light storage plant capacity dynamic allocation method Download PDFInfo
- Publication number
- CN110247411A CN110247411A CN201910412268.9A CN201910412268A CN110247411A CN 110247411 A CN110247411 A CN 110247411A CN 201910412268 A CN201910412268 A CN 201910412268A CN 110247411 A CN110247411 A CN 110247411A
- Authority
- CN
- China
- Prior art keywords
- soc
- photovoltaic
- electricity
- battery
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 230000005611 electricity Effects 0.000 claims abstract description 130
- 238000010248 power generation Methods 0.000 claims abstract description 41
- 230000008901 benefit Effects 0.000 claims abstract description 25
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 241000196324 Embryophyta Species 0.000 description 5
- 238000007599 discharging Methods 0.000 description 3
- 238000004146 energy storage Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 239000010977 jade Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H02J3/383—
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The present invention provides a kind of user side instrumentation intelligence light storage plant capacity dynamic allocation method, including collecting user's history electricity consumption data and being uploaded to server, server carries out ultra-short term electro-load forecast according to user's history electricity consumption data, obtains user's electricity consumption hourly；Server obtains the facility information of intelligent light storage equipment by Internet of Things and obtains Weather information by weather forecast API, and predicts intelligent light using photovoltaic power output model algorithm or photovoltaic simulation software and store up equipment photovoltaic power generation quantity hourly；Server stores up equipment photovoltaic power generation quantity hourly according to the user predicted electricity consumption hourly and intelligent light, automatically analyzes by hour and dynamic adjusts energy allocation strategy；Intelligent light storage equipment obtains from server and updates primary energy allocation strategy every hour, and energy distribution in next hour is executed according to energy allocation strategy.The invention has the advantages that can automatically analyze and dynamic adjusts energy allocation strategy, it can be effective against that photovoltaic power generation is unstable and the variation of user power utilization habit.
Description
Technical field
The present invention relates to energy to distribute field, in particular to a kind of user side instrumentation intelligence light storage plant capacity dynamic point
Method of completing the square.
Background technique
In order to which the energy output of distribution day part, the numerous and confused electricity price for using peak load shifting in all parts of the country at present is better achieved
Strategy imposes different electricity prices for different periods in one day, more expensive in peak times of power consumption electricity price, in low power consumption phase electricity price
It is relatively inexpensive, with this come reach promote user power utilization balance purpose.Appearance for this policy, since the consumption habit of user is real
It is difficult to change on border, therefore the effect obtained is not apparent.
In order to accomplish to maximize save the cost, many users using UPS or energy storage device, which are intended to equipment, to be supported
The function of photovoltaic and peak load shifting.And photovoltaic is referred to and is powered using solar energy clean energy resource, because being influenced by weather, photovoltaic
Power generation situation is uncontrollable.Because how the uncontrollable factor of photovoltaic divides after photovoltaic is added and considers peak load shifting strategy
With energy at important research topic.
With gradualling mature for technology of Internet of things, many equipment are all made of Internet of Things access, to obtain more intelligent function
Energy.Server can be connected using the photovoltaic energy storage equipment of Internet of Things, and obtain stronger operational capability, so as to realize more
Intelligence is added reasonably to customize the energy allocation strategy of each hour.
Energy assignment problem in the case that current energy storage device seldom considers photovoltaic and peak load shifting exists simultaneously,
Generally only a battery charging and discharging scheme is set directly against peak valley.But if user power consumption and photovoltaic power generation quantity change compared with
When big, the power generation that may result in photovoltaic can not be stored or crest segment does not have enough battery capacities to release, cause economic benefit compared with
Difference.If using the strategy protocol of regulation day part in advance, it may be necessary to acquisition user power utilization situation and annual photovoltaic hair in advance
Charge condition, this process is not only cumbersome, complicated, but also needs to be arranged many parameters, and policy is also variable, therefore, real
Now get up very difficult.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of user side instrumentation intelligence light storage plant capacity dynamic point
Method of completing the square, automatically analyzed by this method and dynamic adjust energy allocation strategy, be effective against photovoltaic power generation it is unstable with
And the variation of user power utilization habit.
The present invention is implemented as follows: a kind of user side instrumentation intelligence light stores up plant capacity dynamic allocation method, it is described
Method includes the following steps:
Step S1, it collects user's history electricity consumption data and is uploaded to server, server is according to user's history electricity consumption data
Ultra-short term electro-load forecast is carried out, obtains user's electricity consumption hourly；
Step S2, server obtains intelligent light by Internet of Things and stores up the facility information of equipment and by weather forecast API
Weather information is obtained, and it is hourly to use photovoltaic power output model algorithm or photovoltaic simulation software to predict intelligent light storage equipment
Photovoltaic power generation quantity；
Step S3, server stores up equipment light hourly according to the user predicted electricity consumption hourly and intelligent light
Generated energy is lied prostrate, is automatically analyzed by hour and dynamic adjusts energy allocation strategy；Intelligent light storage equipment is every hour from server
Primary energy allocation strategy is obtained and updated, and executes energy distribution in next hour according to energy allocation strategy.
Further, in the step S3, the server according to the user predicted electricity consumption hourly with
And intelligent light stores up equipment photovoltaic power generation quantity hourly, automatically analyzes by hour and dynamic adjusts energy allocation strategy and specifically wraps
It includes:
Step S31, server sets a minimum deposit electricity SOC to intelligent light storage equipmentups；Server is according to prediction
The electricity consumption hourly of user out and intelligent light store up equipment photovoltaic power generation quantity hourly, with whether can drive completely it is negative
Output is carried as according to by photovoltaic and is divided into photovoltaic high-power output and the output of photovoltaic low-power；
Step S32, when being in photovoltaic high-power output, then control intelligent light storage equipment directly use photovoltaic to load into
Row power supply, and if control intelligent light storage equipment simultaneously there is also extra electricity and charge the battery；
When in the output of photovoltaic low-power, then different energy distribution is automatically generated in conjunction with the different periods of peak load shifting
Strategy.
Further, described when in the output of photovoltaic low-power, then it is given birth to automatically in conjunction with the different periods of peak load shifting
At different energy allocation strategies specifically:
When exporting in photovoltaic low-power and present period is in crest segment electricity price or more, judge whether battery capacity is small
Electricity SOC is laid in being equal to minimumups, and if it is, be powered using photovoltaic and alternating current, make battery standing；If
It is no, then it is powered using photovoltaic and battery；
When in the output of photovoltaic low-power and present period is in flat section electricity price, while next period is paddy section electricity price
When, then judge whether flat Duan Fang electricity, paddy section charging are benefited, and if it is not, then be powered using photovoltaic and alternating current, make battery
It stands；
If it is, judging whether battery capacity is less than or equal to minimum deposit electricity SOCups, if it is, using photovoltaic
It is powered with battery；If it is not, then being powered using photovoltaic and alternating current, make battery standing；
When in the output of photovoltaic low-power and present period is in flat section electricity price, at the same next period be crest segment electricity price and
It when above, then predicts whether the net electric generation of next period is positive, and if it is, calculates continuous net since the next period
The summation that generated energy is positive is able to use photovoltaic charged amount to predict the following continuous time, and using photovoltaic and battery into
Row power supply, until making battery discharge that can extremely store amount photovoltaic charged in the following continuous time, meanwhile, if battery discharge is extremely
Electricity SOC is laid in equal to minimumups, then it is powered using photovoltaic and alternating current；
If it is not, then judging whether flat section charging, crest segment electric discharge benefit, if it is, carrying out using photovoltaic and alternating current
Power supply, and battery is made to charge to the electricity that the continuous crest segment following enough uses；If it is not, then being supplied using photovoltaic and alternating current
Electricity；
When being exported in photovoltaic low-power and present period is in paddy section electricity price, then according to the power generation of future time period and use
Electric situation calculates the bound of the target percentage of battery, remembers that the upper limit of target percentage is SOCtop, remember target percentage
Lower limit is SOCbottom；
And if the current goal percentage of battery is in lower limit SOCbottomTo upper limit SOCtopIn the range of, then use light
Volt and alternating current are powered, and make battery standing；If the current goal percentage of battery is greater than upper limit SOCtop, then light is used
Volt and battery are powered；If the current goal percentage of battery is less than lower limit SOCbottom, then using photovoltaic and alternating current into
Row power supply, and charged the battery with firm power.
It is further, described to judge whether flat Duan Fang electricity, paddy section charging benefit specifically:
Flat section electricity price is set as x1, paddy section electricity price is y1, then if x1- (y1/ ηIt fillsηIt puts) be positive, then illustrate flat Duan Fang electricity,
Paddy section charging can benefit；Otherwise illustrate that flat Duan Fang electricity, paddy section charging cannot benefit.
It is further, described to judge whether flat section charging, crest segment electric discharge benefit specifically:
Flat section electricity price is set as x2, crest segment electricity price is y2, then if y2- (x2/ ηIt fillsηIt puts) be positive, then illustrate the charging of flat section,
Crest segment electric discharge can benefit；Otherwise illustrate that flat section charging, crest segment electric discharge cannot benefit.
Further, the power generation and electricity consumption situation according to future time period calculates the upper of the target percentage of battery
Lower limit specifically includes:
Step A1, the theoretical cell since current time by each hour in this period next paddy section is calculated
Net increment SOC, specifically includes:
The electricity consumption for remembering some period user predicted is E0, the photovoltaic power generation quantity of intelligent light storage equipment is E1；If it is
Period in paddy section electricity price, if E1-E0>=0, then the net increment SOC of theoretical cell is (E1-E0)*ηIt fills/ battery total electricity；If
E1-E0< 0, then the net increment SOC of theoretical cell is 0；
If it is the period for being in non-valley section electricity price, then the net increment SOC of theoretical cell is (E1-E0)*ηIt fills/ battery is always electric
Amount；
According to the calculation of the net increment SOC of above-mentioned theory battery, calculate since current time to next paddy section
The net increment SOC of the theoretical cell of each hour in this period of beginningm, wherein m is natural number；
Step A2, the total net increment ∑ SOC of statistics, specifically includes:
It calculates separately out since current time to total net increment ∑ SOC of n-th of periodn, calculation formula are as follows:Wherein, n is natural number, and n≤m；
Step A3, from calculated each ∑ SOCnIn find out maximum value and minimum value, and according to the maximum value found out and most
Small value calculates the upper limit SOC of battery target percentagetopWith lower limit SOCbottom, specific to calculate are as follows:
SOCtop=100%-max { ∑ SOC1,∑SOC2,∑SOC3,...,∑SOCn}；
SOCbottom=SOCups-min{∑SOC1,∑SOC2,∑SOC3,...,∑SOCn}；
And if calculated SOCtop> 100%, then enable SOCtop=100%；If calculated SOCbottom<SOCups,
Then enable SOCbottom=SOCups；
Step A4, judge whether satisfaction 100% >=SOCtop>=SOCbottom>=SOCups, if it is satisfied, then determining electricity
The range of the target percentage in pond is [SOCbottom, SOCtop]；If conditions are not met, then removing the last one ∑ SOCn, sentence simultaneously
It is disconnected whether only to remain ∑ SOC1, and if it is, enter step A5；If it is not, then return step A3；
Step A5, judge ∑ SOC1Whether less than zero, and if it is, enable SOCtop=SOCbottom=100%；If
It is no, then enable SOCtop=SOCbottom=SOCups。
Further, the step S3 further include:
Intelligent light storage equipment reports actual photovoltaic power generation quantity from trend server by hour, and is sent out using actual photovoltaic
Electricity corrects the photovoltaic power generation quantity of prediction.
The present invention has the advantage that in conclusion the present invention is being embodied, by first using ultra-short term power load
Prediction model to predict user's electricity consumption hourly automatically, while soft using photovoltaic power output model algorithm or photovoltaic emulation
Part stores up equipment photovoltaic power generation quantity hourly to predict intelligent light automatically；Then compared using present period with future time period
Pair mode, and in conjunction with peak load shifting come by hour analyze and dynamically formulate each period energy allocation strategy therefore can have
Effect resists that photovoltaic power generation is unstable and the variation of user power utilization habit；Meanwhile the ginseng that user is various and complicated without setting
Number, because many parameters can be calculated automatically (the net increment of such as battery) by statistical learning, when the use of user
Electricity habit is when changing, can also automatic adjusting parameter, ease for use is greatly improved.
Detailed description of the invention
The present invention is further illustrated in conjunction with the embodiments with reference to the accompanying drawings.
Fig. 1 is the method for the present invention execution flow chart.
Specific embodiment
Refering to Figure 1, a kind of present invention user side instrumentation intelligence light storage plant capacity dynamic allocation method compared with
Good embodiment, described method includes following steps:
Step S1, it collects user's history electricity consumption data and is uploaded to server, server is according to user's history electricity consumption data
Ultra-short term electro-load forecast is carried out, obtains user's electricity consumption hourly；
Step S1 in the specific implementation, can access electric quantity metering chip in the electric energy output end of system, and pass through electricity
Metering chip is measured to record user's electricity consumption hourly, and using the user of record electricity consumption hourly as user's history number
According to being saved in uploading onto the server, to facilitate server that can carry out ultra-short term power load according to user's history data
Prediction.Wherein, for ultra-short term electro-load forecast, have a large amount of paper at present and mathematics supported, such as document 1: Feng Ying,
Modeling in the application-neural network short-term load prediction of Liu Zongqi, Yang Jingyan, Wang Fengxia artificial intelligence in the power system
(1. North China Electric Power University's protecting electrical power systems and key lab, dynamic safety monitored control Ministry of Education Beijing 102206,2. are pacified for analysis
Emblem electric power saving research institute Anhui 230022)；For another example the 6th phase of document 2:2013 appoints permanent outstanding person based on the short-term of bp neural network
Load prediction modeling and simulating.Since the prior art gives very mature technical support to ultra-short term electro-load forecast, because
This, the present invention does not just specifically describe herein.
Step S2, server obtains intelligent light by Internet of Things and stores up the facility information of equipment and by weather forecast API
Weather information is obtained, and it is hourly to use photovoltaic power output model algorithm or photovoltaic simulation software to predict intelligent light storage equipment
Photovoltaic power generation quantity；
In the specific implementation, the facility information of the intelligent light storage equipment of acquisition includes photovoltaic efficiency, sets step S2
Information, the Weather informations of acquisition such as standby position include the information such as solar elevation, weather conditions, cloud amount, temperature, humidity.Due to
The influence factor that photovoltaic power generation is subject to as many as very, therefore, it is necessary to which various facility informations and Weather information are submitted to server,
To facilitate through server the photovoltaic power generation quantity for predicting coming few hours.Wherein, photovoltaic power output model algorithm at present ten
Be divided into it is ripe, for example, document 3: " Proceedings of the CSEE " the 34th phase Yuan Xiao tinkling of pieces of jade Shi Junhua Xu Jie man of virtue and ability in 2013 meter and weather class
The photovoltaic power generation of type index is contributed prediction in short term；Photovoltaic simulation software can use Archelios pro software.
Step S3, server stores up equipment light hourly according to the user predicted electricity consumption hourly and intelligent light
Generated energy is lied prostrate, is automatically analyzed by hour and dynamic adjusts energy allocation strategy；Intelligent light storage equipment is every hour from server
Primary energy allocation strategy is obtained and updated, and executes energy distribution in next hour according to energy allocation strategy.
In the step S3, the server is according to the user predicted electricity consumption hourly and intelligent light storage
Equipment photovoltaic power generation quantity hourly automatically analyzes by hour and dynamic adjusts energy allocation strategy and specifically includes:
Step S31, server sets a minimum deposit electricity SOC to intelligent light storage equipmentups；The intelligence light stores up equipment
Backup power supply function can be taken into account, when carrying out peak load shifting, therefore, to assure that cannot be below minimum deposit electricity SOCups；
Server stores up equipment photovoltaic power generation hourly according to the user predicted electricity consumption hourly and intelligent light
Amount, whether band dynamic load output as foundation photovoltaic can be divided into photovoltaic high-power output and photovoltaic low-power is defeated completely
Out；It i.e. in the specific implementation, is that photovoltaic high power is defeated with regard to explanation if can be exported by photovoltaic come band dynamic load completely
Out, else if can not be exported by photovoltaic come band dynamic load completely, then being the output of photovoltaic low-power with regard to explanation；
Step S32, when being in photovoltaic high-power output, then control intelligent light storage equipment directly use photovoltaic to load into
Row power supply, and if control intelligent light storage equipment simultaneously there is also extra electricity and charge the battery；That is,
When being in photovoltaic high-power output, in order to achieve the purpose that save electric energy, either in which kind of of peak load shifting, all in stage
It is directly to be powered using photovoltaic to load, while extra electricity being stored into battery；
When in the output of photovoltaic low-power, then different energy distribution is automatically generated in conjunction with the different periods of peak load shifting
Strategy, to be effective against, photovoltaic power generation is unstable and the variation of user power utilization habit.
In the present invention, described when in the output of photovoltaic low-power, then it is automatic in conjunction with the different periods of peak load shifting
Generate different energy allocation strategies specifically:
When exporting in photovoltaic low-power and present period is in crest segment electricity price or more, judge whether battery capacity is small
Electricity SOC is laid in being equal to minimumups, and if it is, be powered using photovoltaic and alternating current, make battery standing；If
It is no, then it is powered using photovoltaic and battery；
When in the output of photovoltaic low-power and present period is in flat section electricity price, while next period is paddy section electricity price
When, it is contemplated that the problem of efficiency for charge-discharge, then judge whether flat Duan Fang electricity, paddy section charging benefit, and if it is not, then use photovoltaic
It is powered with alternating current, makes battery standing；
Described judges whether flat Duan Fang electricity, paddy section charging benefit specifically:
Flat section electricity price is set as x1, paddy section electricity price is y1, then if x1- (y1/ ηIt fillsηIt puts) be positive, then illustrate flat Duan Fang electricity,
Paddy section charging can benefit；Otherwise illustrate that flat Duan Fang electricity, paddy section charging cannot benefit；Wherein, ηIt fillsIndicate charge efficiency, ηIt putsIt indicates
Discharging efficiency；
If it is, judging whether battery capacity is less than or equal to minimum deposit electricity SOCups, if it is, using photovoltaic
It is powered with battery；If it is not, then being powered using photovoltaic and alternating current, make battery standing；
When in the output of photovoltaic low-power and present period is in flat section electricity price, at the same next period be crest segment electricity price and
When above, then predict whether the net electric generation of next period is positive and (subtract the photovoltaic power generation quantity of the next period predicted
The electricity consumption of user, whether what is seen is positive number), and if it is, calculate the continuous net electric generation since the next period
The summation being positive from the net electric generation that the next period begins with continuous 3 periods (for example, be positive, then just calculating this 3
The net electric generation summation of period), it is able to use photovoltaic charged amount to predict the following continuous time, and use photovoltaic and battery
It is powered, until making battery discharge that can extremely store amount photovoltaic charged in the following continuous time, meanwhile, if battery discharge
To equal than minimum deposit electricity SOCups(that is, needing to guarantee that the electricity of battery is no less than minimum deposit electricity SOCups),
It is then powered using photovoltaic and alternating current, and no longer discharged battery；
If it is not, then judging whether flat section charging, crest segment electric discharge benefit, if it is, carrying out using photovoltaic and alternating current
Power supply, and battery is made to charge to the electricity that the continuous crest segment following enough uses；If it is not, then being supplied using photovoltaic and alternating current
Electricity；
Described judges whether flat section charging, crest segment electric discharge benefit specifically:
Flat section electricity price is set as x2, crest segment electricity price is y2, then if y2- (x2/ ηIt fillsηIt puts) be positive, then illustrate the charging of flat section,
Crest segment electric discharge can benefit；Otherwise illustrate that flat section charging, crest segment electric discharge cannot benefit, wherein ηIt fillsIndicate charge efficiency, ηIt putsIt indicates
Discharging efficiency；
When being exported in photovoltaic low-power and present period is in paddy section electricity price, then according to the power generation of future time period and use
Electric situation calculates the bound of the target percentage of battery, remembers that the upper limit of target percentage is SOCtop, remember target percentage
Lower limit is SOCbottom；
And if the current goal percentage of battery is in lower limit SOCbottomTo upper limit SOCtopIn the range of, then use light
Volt and alternating current are powered, and make battery standing (not charging the battery, also do not discharge battery)；If battery
Current goal percentage be greater than upper limit SOCtop, then it is powered using photovoltaic and battery；If the current goal percentage of battery
Than being less than lower limit SOCbottom, then it is powered using photovoltaic and alternating current, and charged the battery with firm power.
In the present invention, the power generation and electricity consumption situation according to future time period calculates the target percentage of battery
Bound specifically includes:
Step A1, the theoretical cell since current time by each hour in this period next paddy section is calculated
Net increment SOC, specifically includes:
The electricity consumption for remembering some period user predicted is E0, the photovoltaic power generation quantity of intelligent light storage equipment is E1；If it is
Period in paddy section electricity price, if E1-E0>=0, then the net increment SOC of theoretical cell is (E1-E0)*ηIt fills/ battery total electricity, ηIt fills
Indicate charge efficiency；If E1-E0< 0, then the net increment SOC of theoretical cell is 0；Due to can freely use friendship in paddy section electricity price
Galvanic electricity supplements electricity, and therefore, it is necessary to force theoretical cell net increment SOC when setting paddy section electricity price as nonnegative number；
If it is the period for being in non-valley section electricity price, then the net increment SOC of theoretical cell is (E1-E0)*ηIt fills/ battery is always electric
Amount, ηIt fillsIndicate charge efficiency；
According to the calculation of the net increment SOC of above-mentioned theory battery, calculate since current time to next paddy section
The net increment SOC of the theoretical cell of each hour in this period of beginningm, wherein m is natural number；For example, being opened from current time
5 periods are shared between beginning to next paddy section to start, then the net increment of the theoretical cell for just calculating separately out this 5 periods
SOC1~SOC5；
Step A2, the total net increment ∑ SOC of statistics, specifically includes:
It calculates separately out since current time to total net increment ∑ SOC of n-th of periodn, calculation formula are as follows:Wherein, n is natural number, and n≤m；For example, having calculated that the theory electricity of 5 periods in step A1
The net increment SOC in pond1~SOC5, then, just calculate separately out 5 total net increment ∑ SOC1~∑ SOC5；
Step A3, from calculated each ∑ SOCnIn find out maximum value and minimum value, and according to the maximum value found out and most
Small value calculates the upper limit SOC of battery target percentagetopWith lower limit SOCbottom, specific to calculate are as follows:
SOCtop=100%-max { ∑ SOC1,∑SOC2,∑SOC3,...,∑SOCn}；
SOCbottom=SOCups-min{∑SOC1,∑SOC2,∑SOC3,...,∑SOCn}；
And if calculated SOCtop> 100%, then enable SOCtop=100%；If calculated SOCbottom<SOCups,
Then enable SOCbottom=SOCups；
Step A4, judge whether satisfaction 100% >=SOCtop>=SOCbottom>=SOCups, if it is satisfied, then determining electricity
The range of the target percentage in pond is [SOCbottom, SOCtop]；If conditions are not met, then removing the last one ∑ SOCn, sentence simultaneously
It is disconnected whether only to remain ∑ SOC1, and if it is, enter step A5；If it is not, then return step A3；
Step A5, judge ∑ SOC1Whether less than zero, and if it is, enable SOCtop=SOCbottom=100%；If
It is no, then enable SOCtop=SOCbottom=SOCups。
In the present invention, the step S3 further include:
Intelligent light storage equipment reports actual photovoltaic power generation quantity from trend server by hour, and is sent out using actual photovoltaic
Electricity corrects the photovoltaic power generation quantity of prediction, to facilitate related personnel that can understand coincideing between actual value and predicted value in real time
Degree, and facilitate related personnel and the parameter of photovoltaic power output model is modified, to improve the accuracy of prediction.
In conclusion the present invention is being embodied, by first using ultra-short term electro-load forecast model come automatic Prediction
User's electricity consumption hourly out, while to predict intelligence automatically using photovoltaic power output model algorithm or photovoltaic simulation software
Light stores up equipment photovoltaic power generation quantity hourly；Then it in such a way that present period is compared with future time period, and combines and cuts
Therefore peak load is effective against photovoltaic power generation not to analyze by hour and dynamically formulate the energy allocation strategy of each period
The variation of stable and user power utilization habit；Meanwhile the parameter that user is various and complicated without setting, because many parameters are all
(the net increment of such as battery) can be calculated automatically by statistical learning, when the consumption habit of user changes,
Can also automatic adjusting parameter, ease for use is greatly improved.
Although specific embodiments of the present invention have been described above, those familiar with the art should be managed
Solution, we are merely exemplary described specific embodiment, rather than for the restriction to the scope of the present invention, it is familiar with this
The technical staff in field should be covered of the invention according to modification and variation equivalent made by spirit of the invention
In scope of the claimed protection.
Claims (7)
1. a kind of user side instrumentation intelligence light stores up plant capacity dynamic allocation method, it is characterised in that: the method includes such as
Lower step:
Step S1, it collects user's history electricity consumption data and is uploaded to server, server is carried out according to user's history electricity consumption data
Ultra-short term electro-load forecast obtains user's electricity consumption hourly；
Step S2, server obtains the facility information of intelligent light storage equipment by Internet of Things and is obtained by weather forecast API
Weather information, and intelligent light storage equipment photovoltaic hourly is predicted using photovoltaic power output model algorithm or photovoltaic simulation software
Generated energy；
Step S3, server is sent out according to the user predicted electricity consumption hourly and intelligent light storage equipment photovoltaic hourly
Electricity, automatically analyzes by hour and dynamic adjusts energy allocation strategy；Intelligent light storage equipment is obtained from server every hour
And primary energy allocation strategy is updated, and energy distribution in next hour is executed according to energy allocation strategy.
2. a kind of user side instrumentation intelligence light according to claim 1 stores up plant capacity dynamic allocation method, feature
Be: in the step S3, the server is set according to the user predicted electricity consumption hourly and intelligent light storage
Standby photovoltaic power generation quantity hourly automatically analyzes by hour and dynamic adjusts energy allocation strategy and specifically includes:
Step S31, server sets a minimum deposit electricity SOC to intelligent light storage equipmentups；Server is according to predicting
User's electricity consumption hourly and intelligent light store up equipment photovoltaic power generation quantity hourly, with whether can band dynamic load completely it is defeated
It is divided into photovoltaic high-power output and the output of photovoltaic low-power as according to by photovoltaic out；
Step S32, it when being in photovoltaic high-power output, then controls intelligent light storage equipment and directly load is supplied using photovoltaic
Electricity, and if control intelligent light storage equipment simultaneously there is also extra electricity and charge the battery；
When in the output of photovoltaic low-power, then different energy distribution plans is automatically generated in conjunction with the different periods of peak load shifting
Slightly.
3. a kind of user side instrumentation intelligence light according to claim 2 stores up plant capacity dynamic allocation method, feature
It is: it is described when in the output of photovoltaic low-power, then different energy is automatically generated in conjunction with the different periods of peak load shifting
Allocation strategy specifically:
When exporting in photovoltaic low-power and present period is in crest segment electricity price or more, judge whether battery capacity is less than
Electricity SOC is laid in minimumups, and if it is, be powered using photovoltaic and alternating current, make battery standing；If it is not, then
It is powered using photovoltaic and battery；
When in the output of photovoltaic low-power and present period is in flat section electricity price, while when next period is paddy section electricity price, then
Judge whether flat Duan Fang electricity, paddy section charging are benefited, and if it is not, then be powered using photovoltaic and alternating current, make battery standing；
If it is, judging whether battery capacity is less than or equal to minimum deposit electricity SOCups, if it is, using photovoltaic and electricity
Pond is powered；If it is not, then being powered using photovoltaic and alternating current, make battery standing；
When in the output of photovoltaic low-power and present period is in flat section electricity price, while next period is crest segment electricity price or more
When, then predict whether the net electric generation of next period is positive, and if it is, calculate the continuous net power generation since the next period
The summation being positive is measured, is able to use photovoltaic charged amount to predict the following continuous time, and supplied using photovoltaic and battery
Electricity, until making battery discharge that can extremely store amount photovoltaic charged in the following continuous time, meanwhile, if battery discharge to equal than
Minimum deposit electricity SOCups, then it is powered using photovoltaic and alternating current；
If it is not, then judge whether flat section charging, crest segment electric discharge benefit, if it is, be powered using photovoltaic and alternating current,
And battery is made to charge to the electricity that the continuous crest segment following enough uses；If it is not, then being powered using photovoltaic and alternating current；
When being exported in photovoltaic low-power and present period is in paddy section electricity price, then according to the power generation of future time period and electricity consumption feelings
Condition calculates the bound of the target percentage of battery, remembers that the upper limit of target percentage is SOCtop, remember the lower limit of target percentage
For SOCbottom；
And if the current goal percentage of battery is in lower limit SOCbottomTo upper limit SOCtopIn the range of, then using photovoltaic and
Alternating current is powered, and makes battery standing；If the current goal percentage of battery is greater than upper limit SOCtop, then using photovoltaic and
Battery is powered；If the current goal percentage of battery is less than lower limit SOCbottom, then supplied using photovoltaic and alternating current
Electricity, and charged the battery with firm power.
4. a kind of user side instrumentation intelligence light according to claim 3 stores up plant capacity dynamic allocation method, feature
Be: described judges whether flat Duan Fang electricity, paddy section charging benefit specifically:
Flat section electricity price is set as x1, paddy section electricity price is y1, then if x1- (y1/ ηIt fillsηIt puts) be positive, then illustrate flat Duan Fang electricity, paddy section
Charging can benefit；Otherwise illustrate that flat Duan Fang electricity, paddy section charging cannot benefit.
5. a kind of user side instrumentation intelligence light according to claim 3 stores up plant capacity dynamic allocation method, feature
Be: described judges whether flat section charging, crest segment electric discharge benefit specifically:
Flat section electricity price is set as x2, crest segment electricity price is y2, then if y2- (x2/ ηIt fillsηIt puts) be positive, then illustrate flat section charging, crest segment
Electric discharge can benefit；Otherwise illustrate that flat section charging, crest segment electric discharge cannot benefit.
6. a kind of user side instrumentation intelligence light according to claim 3 stores up plant capacity dynamic allocation method, feature
Be: the bound for the target percentage that the power generation and electricity consumption situation according to future time period calculates battery is specifically wrapped
It includes:
Step A1, the theoretical cell since current time by each hour in this period next paddy section is calculated to have a net increase of
Long amount SOC, specifically includes:
The electricity consumption for remembering some period user predicted is E0, the photovoltaic power generation quantity of intelligent light storage equipment is E1；If it is being in
The period of paddy section electricity price, if E1-E0>=0, then the net increment SOC of theoretical cell is (E1-E0)*ηIt fills/ battery total electricity；If E1-E0
< 0, then the net increment SOC of theoretical cell is 0；
If it is the period for being in non-valley section electricity price, then the net increment SOC of theoretical cell is (E1-E0)*ηIt fills/ battery total electricity；
According to the calculation of the net increment SOC of above-mentioned theory battery, calculate since current time to next paddy section
This period in each hour the net increment SOC of theoretical cellm, wherein m is natural number；
Step A2, the total net increment ∑ SOC of statistics, specifically includes:
It calculates separately out since current time to total net increment ∑ SOC of n-th of periodn, calculation formula are as follows:Wherein, n is natural number, and n≤m；
Step A3, from calculated each ∑ SOCnIn find out maximum value and minimum value, and according to the maximum value and minimum value meter found out
Calculate the upper limit SOC of battery target percentagetopWith lower limit SOCbottom, specific to calculate are as follows:
SOCtop=100%-max { ∑ SOC1,∑SOC2,∑SOC3,...,∑SOCn}；
SOCbottom=SOCups-min{∑SOC1,∑SOC2,∑SOC3,...,∑SOCn}；
And if calculated SOCtop> 100%, then enable SOCtop=100%；If calculated SOCbottom<SOCups, then enable
SOCbottom=SOCups；
Step A4, judge whether satisfaction 100% >=SOCtop>=SOCbottom>=SOCups, if it is satisfied, then determining battery
The range of target percentage is [SOCbottom, SOCtop]；If conditions are not met, then removing the last one ∑ SOCn, while judgement is
No surplus ∑ SOC1, and if it is, enter step A5；If it is not, then return step A3；
Step A5, judge ∑ SOC1Whether less than zero, and if it is, enable SOCtop=SOCbottom=100%；If it is not, then enabling
SOCtop=SOCbottom=SOCups。
7. a kind of user side instrumentation intelligence light according to claim 1 stores up plant capacity dynamic allocation method, feature
It is: the step S3 further include:
Intelligent light storage equipment reports actual photovoltaic power generation quantity from trend server by hour, and uses actual photovoltaic power generation quantity
To correct the photovoltaic power generation quantity of prediction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910412268.9A CN110247411B (en) | 2019-05-17 | 2019-05-17 | Dynamic energy distribution method for user-side Internet of things intelligent optical storage equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910412268.9A CN110247411B (en) | 2019-05-17 | 2019-05-17 | Dynamic energy distribution method for user-side Internet of things intelligent optical storage equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110247411A true CN110247411A (en) | 2019-09-17 |
| CN110247411B CN110247411B (en) | 2023-06-27 |
Family
ID=67884489
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910412268.9A Active CN110247411B (en) | 2019-05-17 | 2019-05-17 | Dynamic energy distribution method for user-side Internet of things intelligent optical storage equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110247411B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111711189A (en) * | 2020-06-18 | 2020-09-25 | 华润协鑫(北京)热电有限公司 | Photovoltaic power generation energy storage control method, system, equipment and storage medium |
| CN112070301A (en) * | 2020-09-07 | 2020-12-11 | 广东电网有限责任公司电力调度控制中心 | Method, system and equipment for adjusting power consumption of user |
| CN113098038A (en) * | 2021-04-06 | 2021-07-09 | 中国电建集团贵州电力设计研究院有限公司 | Photovoltaic hydrogen production energy storage system and energy storage method |
| CN113515831A (en) * | 2021-07-30 | 2021-10-19 | 广东电网有限责任公司 | Energy scheduling method and device, electronic equipment and storage medium |
| CN114301093A (en) * | 2021-12-28 | 2022-04-08 | 中建科技集团北京低碳智慧城市科技有限公司 | Photovoltaic microgrid electric energy supply control method, device, equipment and storage medium |
| CN115051415A (en) * | 2022-08-15 | 2022-09-13 | 苏州思萃融合基建技术研究所有限公司 | AI prediction-based power distribution strategy decision method and device for light storage direct and flexible system |
| CN116154969A (en) * | 2023-03-06 | 2023-05-23 | 珠海赣星自动化设备有限公司 | Intelligent power grid integrated management system |
| CN116562657A (en) * | 2023-07-12 | 2023-08-08 | 苏州精控能源科技有限公司 | Photovoltaic energy storage management method and device based on Internet of things, medium and electronic equipment |
| WO2024060047A1 (en) * | 2022-09-21 | 2024-03-28 | Citrix Systems, Inc. | Intelligent determination of required battery levels for battery-operated devices |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106786547A (en) * | 2017-01-12 | 2017-05-31 | 沃太能源南通有限公司 | A kind of new micro-grid system and the networking scheduling method based on the system |
| CN109245085A (en) * | 2018-10-11 | 2019-01-18 | 福建星云电子股份有限公司 | DC energy storage backup power supply and control method with peak load shifting function |
-
2019
- 2019-05-17 CN CN201910412268.9A patent/CN110247411B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106786547A (en) * | 2017-01-12 | 2017-05-31 | 沃太能源南通有限公司 | A kind of new micro-grid system and the networking scheduling method based on the system |
| CN109245085A (en) * | 2018-10-11 | 2019-01-18 | 福建星云电子股份有限公司 | DC energy storage backup power supply and control method with peak load shifting function |
Non-Patent Citations (4)
| Title |
|---|
| 李江等: "分布式光储微电网系统并网控制策略研究", 《电力系统保护与控制》 * |
| 李龙坤等: "独立光伏微网容量优化配置", 《山东电力技术》 * |
| 江磊等: "分时电价下直流微网优化运行和容量配置研究", 《电力科学与技术学报》 * |
| 田兵等: "用户侧微电网能量优化策略与工程实践", 《南方电网技术》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111711189A (en) * | 2020-06-18 | 2020-09-25 | 华润协鑫(北京)热电有限公司 | Photovoltaic power generation energy storage control method, system, equipment and storage medium |
| CN112070301A (en) * | 2020-09-07 | 2020-12-11 | 广东电网有限责任公司电力调度控制中心 | Method, system and equipment for adjusting power consumption of user |
| CN113098038A (en) * | 2021-04-06 | 2021-07-09 | 中国电建集团贵州电力设计研究院有限公司 | Photovoltaic hydrogen production energy storage system and energy storage method |
| CN113515831A (en) * | 2021-07-30 | 2021-10-19 | 广东电网有限责任公司 | Energy scheduling method and device, electronic equipment and storage medium |
| CN113515831B (en) * | 2021-07-30 | 2023-07-18 | 广东电网有限责任公司 | Energy scheduling method and device, electronic equipment and storage medium |
| CN114301093A (en) * | 2021-12-28 | 2022-04-08 | 中建科技集团北京低碳智慧城市科技有限公司 | Photovoltaic microgrid electric energy supply control method, device, equipment and storage medium |
| CN115051415B (en) * | 2022-08-15 | 2022-10-28 | 苏州思萃融合基建技术研究所有限公司 | AI prediction-based power distribution strategy decision method and device for light storage direct-flexible system |
| CN115051415A (en) * | 2022-08-15 | 2022-09-13 | 苏州思萃融合基建技术研究所有限公司 | AI prediction-based power distribution strategy decision method and device for light storage direct and flexible system |
| WO2024060047A1 (en) * | 2022-09-21 | 2024-03-28 | Citrix Systems, Inc. | Intelligent determination of required battery levels for battery-operated devices |
| CN116154969A (en) * | 2023-03-06 | 2023-05-23 | 珠海赣星自动化设备有限公司 | Intelligent power grid integrated management system |
| CN116154969B (en) * | 2023-03-06 | 2023-08-29 | 珠海赣星自动化设备有限公司 | Intelligent power grid integrated management system |
| CN116562657A (en) * | 2023-07-12 | 2023-08-08 | 苏州精控能源科技有限公司 | Photovoltaic energy storage management method and device based on Internet of things, medium and electronic equipment |
| CN116562657B (en) * | 2023-07-12 | 2023-09-12 | 苏州精控能源科技有限公司 | Photovoltaic energy storage management method and device based on Internet of things, medium and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110247411B (en) | 2023-06-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110247411A (en) | A kind of user side instrumentation intelligence light storage plant capacity dynamic allocation method | |
| Yang et al. | Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review | |
| Azuatalam et al. | Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation | |
| e Silva et al. | Photovoltaic self-sufficiency of Belgian households using lithium-ion batteries, and its impact on the grid | |
| Dufo-López et al. | Optimisation of PV-wind-diesel-battery stand-alone systems to minimise cost and maximise human development index and job creation | |
| CN109103912B (en) | Industrial park active power distribution system scheduling optimization method considering power grid peak regulation requirements | |
| Arias et al. | Multi-objective sizing of battery energy storage systems for stackable grid applications | |
| Upadhyay et al. | A review on configurations, control and sizing methodologies of hybrid energy systems | |
| Zhao et al. | Optimal sizing, operating strategy and operational experience of a stand-alone microgrid on Dongfushan Island | |
| CN110969284B (en) | Double-layer optimized scheduling method for power distribution network | |
| Salimi et al. | Simultaneous operation of wind and pumped storage hydropower plants in a linearized security-constrained unit commitment model for high wind energy penetration | |
| Chen et al. | Optimal allocation of distributed generation and energy storage system in microgrids | |
| Chok et al. | Novel fuzzy-based control strategy for standalone power systems for minimum cost of electricity in rural areas | |
| Yuan et al. | Bess aided renewable energy supply using deep reinforcement learning for 5g and beyond | |
| Georgiou et al. | A genetic algorithm driven linear programming for battery optimal scheduling in nearly zero energy buildings | |
| CN115811135A (en) | Intelligent monitoring and regulation system for charging and battery replacing equipment based on power grid peak regulation and frequency modulation | |
| Ahlawat et al. | Optimal sizing and scheduling of battery energy storage system with solar and wind DG under seasonal load variations considering uncertainties | |
| CN113937752A (en) | Active power distribution network optimal scheduling method and system based on stochastic model predictive control | |
| CN111898801B (en) | Method and system for configuring multi-energy complementary power supply system | |
| Shi et al. | Optimal allocation of energy storage capacity for hydro-wind-solar multi-energy renewable energy system with nested multiple time scales | |
| TW201915838A (en) | Particle swarm optimization (PSO) fuzzy logic control (FLC) charging method applicable to smart grid in which a current-state-of-charge input membership function and a state-of-charge-variation input membership function are used to provide fuzzy results through a first and a second fuzzy operations | |
| Hua et al. | Design of energy dispatch strategy of active distribution network using chance-constrained programming | |
| CN106655175B (en) | A kind of resident's electricity consumption intelligent scheduling optimization method | |
| CN105071421B (en) | Office Building Energy Consumption management method | |
| CN111478325B (en) | Energy scheduling method and system for hydrogen-electricity hybrid energy storage microgrid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |