CN106953318A - A kind of micro-capacitance sensor optimal control method based on cost - Google Patents
A kind of micro-capacitance sensor optimal control method based on cost Download PDFInfo
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- CN106953318A CN106953318A CN201710198937.8A CN201710198937A CN106953318A CN 106953318 A CN106953318 A CN 106953318A CN 201710198937 A CN201710198937 A CN 201710198937A CN 106953318 A CN106953318 A CN 106953318A
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- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000004146 energy storage Methods 0.000 claims abstract description 42
- 240000002853 Nelumbo nucifera Species 0.000 claims abstract description 18
- 235000006508 Nelumbo nucifera Nutrition 0.000 claims abstract description 18
- 235000006510 Nelumbo pentapetala Nutrition 0.000 claims abstract description 18
- 230000033228 biological regulation Effects 0.000 claims abstract description 12
- 238000005457 optimization Methods 0.000 claims abstract description 5
- 230000000087 stabilizing effect Effects 0.000 claims abstract description 4
- 230000005611 electricity Effects 0.000 claims description 21
- 230000007774 longterm Effects 0.000 claims description 7
- 238000012423 maintenance Methods 0.000 claims description 7
- 238000005286 illumination Methods 0.000 claims description 5
- 238000007599 discharging Methods 0.000 claims description 4
- 101150067055 minC gene Proteins 0.000 claims description 3
- 238000005553 drilling Methods 0.000 claims description 2
- 230000007613 environmental effect Effects 0.000 claims description 2
- 238000001556 precipitation Methods 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
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- 238000005516 engineering process Methods 0.000 description 1
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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- H02J3/382—
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/388—Islanding, i.e. disconnection of local power supply from the network
Abstract
The invention discloses a kind of micro-capacitance sensor optimal control method based on cost, this method predicts the positive and negative load curve of lotus end load in micro-capacitance sensor, energy storage device and distributed power source first；Then the power price curve of external electrical network is set up, the discharge and recharge cost function of energy storage device and the cost of electricity-generating function of distributed power source is set up, sets up demand response cost function；Again so that regulation and control cost is minimum, external electrical network power purchase curve keeps stabilizing to constraints, optimisation strategy scheme is generated；The power curve of energy storage device and distributed power source, adjusts lotus end load in last real-time optimization micro-capacitance sensor, balances the load curve of micro-capacitance sensor.The present invention is calculated by all kinds of regulation and control cost of electricity-generatings, is realized that power is reasonably distributed based on regulation and control cost, is realized the economy system operation of micro-capacitance sensor.
Description
Technical field
The present invention relates to micro-capacitance sensor field, more particularly to a kind of micro-capacitance sensor optimal control method based on cost.
Background technology
Micro-capacitance sensor (Micro-Grid) refer to by distributed power source, energy storage device, energy conversion device, load, monitoring and
The small-sized electric system of the compositions such as protection device.Micro-capacitance sensor, which is one, can realize the autonomy of self-contr ol, protection and management
System, can both be incorporated into the power networks with external electrical network, can also isolated operation.The proposition of micro-capacitance sensor aims at distributed power source
Flexibly, efficient application, solves substantial amounts, the grid-connected problem of various informative distributed power source.
In micro-capacitance sensor, the permeability more and more higher that the clean energy resource such as wind-force, photovoltaic generates electricity is exerted oneself due to regenerative resource
Fluctuation, intermittence and uncertainty, it is current micro-capacitance sensor needs that how the microgrid containing more new energy, which carries out operation control,
The problem of solution.Solution based on energy storage device is more universal at present, and this solution is set using energy storage such as batteries
The targets such as standby economical operation and control and suppression new energy fluctuation to realize microgrid.Merely can be effective using energy storage device
Microgrid operation is controlled, but construction cost is relatively high, and also the life-span is extremely limited in the case of frequent use, can increase micro-
The cost that net is built and used.Regulation for the purpose of also having some in addition in the way of ensureing system power balance with frequency stabilization
Mode, it is impossible to realize that power is reasonably distributed, the economy of system operation is poor.
The content of the invention
To overcome problem of the prior art, the present invention provides a kind of micro-capacitance sensor optimal control method based on cost.
The purpose of the present invention is achieved through the following technical solutions：A kind of micro-capacitance sensor optimal control side based on cost
Method, methods described includes：
(1) lotus end load, energy storage device, external electrical network and distributed power source are exerted oneself in the micro-capacitance sensor of collection micro-grid system
Information.
(2) the positive and negative load curve of lotus end load, energy storage device and distributed power source in the micro-capacitance sensor is predicted.
(3) the power price curve of external electrical network is set up, the discharge and recharge cost function and distributed electrical of energy storage device is set up
The cost of electricity-generating function in source, sets up demand response cost function.
(4) capacity vacancy is calculated, it is raw so that regulation and control cost is minimum, external electrical network power purchase curve keeps stabilizing to constraints
Into optimisation strategy scheme.
(5) in real-time optimization micro-capacitance sensor energy storage device and distributed power source power curve, adjust lotus end load, balance it is micro-
The load curve of power network.
Beneficial effects of the present invention are mainly shown as：The present invention compares calculating by all kinds of regulation and control cost of electricity-generatings, based on tune
Control cost realizes that power is reasonably distributed, and realizes the economy system operation of micro-capacitance sensor.
Brief description of the drawings
Fig. 1 is a kind of flow chart for micro-capacitance sensor optimal control method based on cost that the present invention is provided.
Embodiment
The present invention is described in further detail with embodiment below in conjunction with the accompanying drawings.
It is a kind of micro-capacitance sensor optimal control method based on cost that embodiments in accordance with the present invention are provided referring to the drawings 1
Flow chart.
Step S101, collection micro-grid system includes lotus end load, energy storage device, external electrical network and distribution in micro-capacitance sensor
The information that power supply is exerted oneself；
Charging and discharging currents, charging/discharging voltage and charge/discharge capacity of the energy storage device information of collection including energy storage device,
Charge and discharge number of times；The external electrical network information of collection includes voltage, frequency and the external electrical network electricity price of external electrical network；The distribution of collection
Formula power supply is exerted oneself the power output of information including distributed power source, output voltage.
Step S102, predicts the positive and negative load curve of lotus end load, energy storage device and distributed power source in the micro-capacitance sensor；
Predict that lotus end load includes in the micro-capacitance sensor：
Historical data base is set up, the lotus first day of the lunar year normal information on load in micro-capacitance sensor, including micro-capacitance sensor internal loading data, electricity is collected
Valency, temperature, humidity, intensity of illumination, wind speed, whether festivals or holidays, emergency information.
Reject illegal day data, including the load data date lacked, the day that has a power failure, maintenance day.
Using Euclidean distance to predicting that the load data and history daily load data of day are calculated.
Calculation procedure includes：The conditional parameter of input is normalized, the conditional parameter of input includes：Temperature,
Intensity of illumination, wind speed, whether festivals or holidays, emergency information；
UsingCalculate the similar of prediction day and history day
Degree, wherein x are to predict the weather data of day, and y is the weather data of history day；
When d (x, y) result of calculation is minimum, the two degree of approximation is higher, obtains the phase close with prediction day weather conditions
Like day.
Using ARMAX forecast models to predicting that the load data of day is predicted, ARMAX forecast model expression formulas are as follows：
In formula (1), y (t) represents the payload of prediction day t, and the load that y (t-1) represents similar day t is big
It is small, z (t-1)jIt is the variable for predicting day external world's input, d represents to predict the number of day extraneous input variable, including power taking valency, day
Phase, indoor and outdoor temperature, humidity, the precipitation factor, intensity of illumination, wind speed；E (t) and e (t+1) represents respectively t noise figure with
Following error；L, m, n are the exponent number of automatic returning, extraneous input and moving average recurrence, Φ (B), ψ (B) and Ω (B) points respectively
Not Biao Shi AR parts, extraneous input, the parameter of MA parts, can be expressed as：
ψ (B)=1+ θ1B+θ2B2+…+θnBn (3)
Ω (B)=1+ ω1B+ω2B2+…+ωmBm (4)
Wherein B is single order backward shift operator, meets BiY (t)=y (t-i).
Step S103, sets up the power price curve of external electrical network, the discharge and recharge cost function of energy storage device and distribution
The cost of electricity-generating function of power supply, sets up demand response cost function；
Long-term electricity price price curve in acquisition, medium-term and long-term electricity price is buyer and the seller long-term contract in signing in electricity market
Defined power price, is P for electricity priceL。
Obtain the electricity price information of external electrical network and store, T period was divided into by 24 hours one day, during for any t
Carve, there are t ∈ { 1,2 ..., T }, correspondence electricity price is Pe, when a length of Δ t of t periods, the power price song of drafting external electrical network
Line.
Before the cost of electricity-generating function of energy storage device and distributed power source is set up, obtain energy storage device single discharge and recharge into
Originally with the unit cost of electricity-generating of distributed power source, the unit cost of electricity-generating of distributed power source can be expressed as：
In formula (5), BdeIt is the cost of electricity-generating of distributed power source, I0It is distributed power source cost of investment, AnIt is the fortune of 1 year
Seek maintenance cost, DnIt is the depreciation of 1 year, BeIt is the Environmental costs of unit generated energy, YnIt is the generated energy of 1 year.
The unit discharge and recharge cost of energy storage device can be expressed as：
In formula (6), BscIt is the unit discharge and recharge cost of energy storage device, I1It is the cost of investment of energy storage device, AmIt is m
Operation maintenance cost, DmIt is m depreciation, a is the charge and discharge cycles number of times of energy storage device, PscIt is energy storage device capacity, θ
It is the efficiency for charge-discharge of energy storage device, efficiency for charge-discharge refers to the capacity ratio that battery is charged and discharged.
The unit capacity cost of demand response can be expressed as：
In formula (7), BdrIt is the unit capacity cost of demand response, I2It is the cost of investment of demand response, AoIt is o
Operation maintenance cost, DoIt is o depreciation, L is the expected peak total load amount cut down, BrThe user of specific load is excited into
This.
Step S104, calculates capacity vacancy, so that regulation and control cost is minimum, external electrical network power purchase curve keeps stabilizing to constraint bar
Part, generates optimisation strategy scheme；
The computing formula of capacity vacancy can be expressed as：
Δ P=| PGenerate electricity-PLoad|, PGenerate electricityIt is the generating total load of distributed power source, PLoadFor prediction user power utilization load.
The cost of external electrical network power purchase, energy storage device discharge and recharge, distributed power source generating and demand response is calculated respectively, can
It is expressed as：
Formula 8) in Δ P1It is external electrical network power purchase load, Δ P2It is energy storage device electric discharge load, Δ P3It is distributed power source hair
Electric load, Δ P4It is the load that demand response is cut down, minC is the totle drilling cost of regulation and control, and min D are closest to long-term contract electricity price
Outside purchase electricity price.
In embodiment, such as user type is large user and possesses medium-term and long-term electricity price contract, being discharged using energy storage device,
After distributed power source generating, demand response are cut down, residue works as P to external electrical network power purchaseeOutside purchase electricity price and medium-term and long-term contract
Electricity price PLClosest to when, resulting control methods are Optimal regulation and control mode.
Compare the size of different control methods costs, obtain the minimum control methods of cost, and generate regulation and control instruction.
The power curve of energy storage device and distributed power source in step S105, real-time optimization micro-capacitance sensor, adjusts lotus end load,
Balance the load curve of micro-capacitance sensor.
Calculated according to step S104 obtain minC regulate and control in minimum laod sharing mode, optimization micro-capacitance sensor energy storage device and
The power curve of distributed power source, adjusts lotus end load, balances the load curve of micro-capacitance sensor.
Above-described embodiment is used for that the invention will be further described, but does not limit the invention to these specific embodiment parties
Formula.One skilled in the art would recognize that all alternative present invention encompasses what is potentially included in Claims scope
Scheme, improvement project and equivalents.
Claims (10)
1. a kind of micro-capacitance sensor optimal control method based on cost, it is characterised in that methods described includes：
(1) letter that lotus end load, energy storage device, external electrical network and distributed power source are exerted oneself in the micro-capacitance sensor of collection micro-grid system
Breath.
(2) the positive and negative load curve of lotus end load, energy storage device and distributed power source in the micro-capacitance sensor is predicted.
(3) set up the power price curve of external electrical network, set up the discharge and recharge cost function and distributed power source of energy storage device
Cost of electricity-generating function, sets up demand response cost function.
(4) capacity vacancy is calculated, so that regulation and control cost is minimum, external electrical network power purchase curve keeps stabilizing to constraints, is generated excellent
Change strategy protocol.
(5) in real-time optimization micro-capacitance sensor energy storage device and distributed power source power curve, adjust lotus end load, balance micro-capacitance sensor
Load curve.
2. a kind of micro-capacitance sensor optimal control method based on cost as claimed in claim 1, it is characterised in that the step 2
In, predict that lotus end load includes following sub-step in the micro-capacitance sensor：
(2.1) historical data base is set up, the lotus first day of the lunar year normal information on load in micro-capacitance sensor, including micro-capacitance sensor internal loading data, electricity is collected
Valency, temperature, humidity, intensity of illumination, wind speed, whether festivals or holidays, emergency information etc..
(2.2) illegal day data, including the load data date lacked, the day that has a power failure, maintenance day etc. are rejected.
(2.3) obtained and in advance using Euclidean distance to predicting that the load data and history daily load data of day are calculated
The close similar day of survey day weather conditions.
(2.4) using ARMAX forecast models to predicting that the load data of day is predicted.
3. a kind of micro-capacitance sensor optimal control method based on cost as claimed in claim 2, it is characterised in that the use Europe
Distance is obtained in several to predicting that the load data and historical load data of day carry out calculation procedure, is specially：
From historical data base, join the lotus first day of the lunar year normal information on load in the micro-capacitance sensor for predicting day and history day as the condition of input
Number；
The conditional parameter of input is normalized；
The similarity degree of prediction day and history day is calculated using Euclidean distance formula；
When result of calculation is minimum, final similar day data are obtained.
4. a kind of micro-capacitance sensor optimal control method based on cost as claimed in claim 2, it is characterised in that the ARMAX
Forecast model expression formula is as follows：
In formula (1), y (t) represents the payload of prediction day t, and y (t-1) represents the payload of similar day t, z
(t-1)jIt is the variable for predicting day external world's input, d represents to predict the number of day extraneous input variable, including power taking valency, date, room
Internal and external temperature, humidity, the precipitation factor, intensity of illumination, wind speed；E (t) and e (t+1) represent t noise figure and future respectively
Error；L, m, n are the exponent number of automatic returning, extraneous input and moving average recurrence, Φ (B), ψ (B) and Ω (B) difference tables respectively
Show AR parts, extraneous input, the parameter of MA parts.
5. a kind of micro-capacitance sensor optimal control method based on cost as claimed in claim 1, it is characterised in that the step 3
In, obtain the electricity price information of external electrical network and store, be divided into T period by 24 hours one day, for any t, there is t
∈ { 1,2 ..., T }, correspondence electricity price is Pe, when a length of Δ t of t periods, the power price curve of drafting external electrical network.
6. a kind of micro-capacitance sensor optimal control method based on cost as claimed in claim 1, it is characterised in that the step 1
In, the collection energy storage device information includes charging and discharging currents, charging/discharging voltage and the charge/discharge capacity of energy storage device, charge and discharge
Number of times；The information of the collection external electrical network includes voltage, frequency and external electrical network electricity price of external electrical network etc.；The collection
Distributed power source is exerted oneself power output, output voltage etc. of information including distributed power source.
7. a kind of micro-capacitance sensor optimal control method based on cost as claimed in claim 6, it is characterised in that the distribution
The unit cost of electricity-generating of power supply can be expressed as：
In formula (5), BdeIt is the cost of electricity-generating of distributed power source, I0It is distributed power source cost of investment, AnBe 1 year operation dimension
Protect cost, DnIt is the depreciation of 1 year, BeIt is the Environmental costs of unit generated energy, YnIt is the generated energy of 1 year.
8. a kind of micro-capacitance sensor optimal control method based on cost as claimed in claim 6, it is characterised in that the energy storage is set
Standby unit discharge and recharge cost can be expressed as：
In formula (6), BscIt is the unit discharge and recharge cost of energy storage device, I1It is the cost of investment of energy storage device, AmIt is m fortune
Seek maintenance cost, DmIt is m depreciation, a is the charge and discharge cycles number of times of energy storage device, PscIt is energy storage device capacity, θ is storage
The efficiency for charge-discharge of energy equipment.
9. a kind of micro-capacitance sensor optimal control method based on cost as claimed in claim 1, it is characterised in that demand response
Unit capacity cost can be expressed as：
In formula (7), BdrIt is the unit capacity cost of demand response, I2It is the cost of investment of demand response, AoIt is o operation
Maintenance cost, DoIt is o depreciation, L is the expected peak total load amount cut down, BrUser's incentive cost of specific load.
10. a kind of micro-capacitance sensor optimal control method based on cost as claimed in claim 1, it is characterised in that the step
4) in, the computing formula of capacity vacancy can be expressed as：
Δ P=| PGenerate electricity-PLoad|, PGenerate electricityIt is the generating total amount of distributed power source, PLoadFor prediction user power utilization load；
The cost of external electrical network power purchase, energy storage device discharge and recharge, distributed power source generating and demand response is calculated respectively, can be represented
For：
Formula 8) in Δ P1It is external electrical network power purchase load, Δ P2It is energy storage device electric discharge load, Δ P3It is that distributed power source generating is negative
Lotus, Δ P4It is the load that demand response is cut down, PLFor medium-term and long-term electricity price, minC is the totle drilling cost of regulation and control, and min D are closest to length
The outside purchase electricity price of phase contract electricity price.
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Cited By (4)
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| CN109167368A (en) * | 2018-09-18 | 2019-01-08 | 国网湖南省电力有限公司 | A kind of user's voltage optimization adjusting method and system containing distributed photovoltaic |
| CN109615151A (en) * | 2019-01-08 | 2019-04-12 | 广东工业大学 | A kind of prediction technique, device and the medium of the double optimizations of load energy storage |
| CN110061489A (en) * | 2019-05-28 | 2019-07-26 | 河南城建学院 | A kind of control system of direct-current grid |
| CN111200288A (en) * | 2020-01-07 | 2020-05-26 | 武汉烽火富华电气有限责任公司 | Park microgrid system demand response method based on neural network |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109167368A (en) * | 2018-09-18 | 2019-01-08 | 国网湖南省电力有限公司 | A kind of user's voltage optimization adjusting method and system containing distributed photovoltaic |
| CN109167368B (en) * | 2018-09-18 | 2020-05-15 | 国网湖南省电力有限公司 | User voltage optimization and regulation method and system with distributed photovoltaic |
| CN109615151A (en) * | 2019-01-08 | 2019-04-12 | 广东工业大学 | A kind of prediction technique, device and the medium of the double optimizations of load energy storage |
| CN109615151B (en) * | 2019-01-08 | 2023-01-03 | 广东工业大学 | Load energy storage double-optimization prediction method, device and medium |
| CN110061489A (en) * | 2019-05-28 | 2019-07-26 | 河南城建学院 | A kind of control system of direct-current grid |
| CN111200288A (en) * | 2020-01-07 | 2020-05-26 | 武汉烽火富华电气有限责任公司 | Park microgrid system demand response method based on neural network |
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Application publication date: 20170714 |