CN106230007B - A kind of micro-capacitance sensor energy storage Optimization Scheduling - Google Patents
A kind of micro-capacitance sensor energy storage Optimization Scheduling Download PDFInfo
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- CN106230007B CN106230007B CN201610590563.XA CN201610590563A CN106230007B CN 106230007 B CN106230007 B CN 106230007B CN 201610590563 A CN201610590563 A CN 201610590563A CN 106230007 B CN106230007 B CN 106230007B
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- 238000004146 energy storage Methods 0.000 title claims abstract description 39
- 238000005457 optimization Methods 0.000 title claims abstract description 16
- 230000005611 electricity Effects 0.000 claims abstract description 54
- 238000000034 method Methods 0.000 claims abstract description 21
- 238000010248 power generation Methods 0.000 claims abstract description 13
- 238000007599 discharging Methods 0.000 claims abstract description 9
- 230000007704 transition Effects 0.000 claims abstract description 6
- 238000009825 accumulation Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 claims description 2
- 238000012706 support-vector machine Methods 0.000 claims description 2
- 230000004044 response Effects 0.000 description 5
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000002457 bidirectional effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
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
- H02J3/28—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
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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]
-
- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- 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
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
- Y02P80/14—District level solutions, i.e. local energy networks
Abstract
The present invention provides a kind of micro-capacitance sensor energy storage Optimization Scheduling, and to optimizing scheduling with the intelligent micro-grid structure for exchanging the electricity consumption type and AC/DC changeover switch that mix including direct current, the dispatching method includes the following steps：Step 1 predicts next day load and photovoltaic, wind turbine power generation, step 2 establishes Optimized model to the transition status of AC/DC changeover switch and the charging and discharging state of energy-storage battery based on load and photovoltaic, the prediction result of wind turbine power generation and electricity price with total electricity bill minimum, and step 3 optimizes Optimized model；The present invention considers AC load and DC load simultaneously, and the charging and discharging state of transition status and energy-storage battery to AC/DC changeover switch optimizes, and then realizes that the purpose of power cost saving, the present invention are suitable for different types of micro-capacitance sensor user.
Description
Technical field
The present invention relates to a kind of micro-capacitance sensor energy storage Optimization Schedulings, belong to new energy and electric power demand side response field.
Background technology
With the development of intelligent grid and universal, the following small-scale generation of electricity by new energy of low-carbon technology, energy storage device, heat
Pump etc. will be used widely in power consumer.These technologies not only affect original power system operating mode, also give and use
For family side electric energy using the more flexibilities brought, user can be with the price incentive signal of responsive electricity grid to change its electricity consumption row
For i.e. Demand Side Response.Power grid Peak power use can be reduced by Demand Side Response, promotes the electricity consumption of paddy phase.Micro-capacitance sensor energy storage is excellent
It is to realize the important means of user side Demand Side Response to change scheduling system, is increasingly taken seriously at present.Micro-capacitance sensor energy storage
The advantage of Optimal Scheduling is：
(1) it can be used by coordinating local power generation and with the electrically optimized energy, promote energy use efficiency；
(2) energy efficiency is promoted by introducing DC load；
(3) by Demand Side Response peak load shifting, electrical network economy type and safety are promoted；
(4) to the direct economy income of terminal user.
Micro-capacitance sensor energy storage Optimal Scheduling is the use by configuring electric energy to the meaning of user's most worthy, can be with
By electricity consumption of the user when electricity price is high be transferred to electricity price it is low when electricity consumption, to realize the purpose of power cost saving, current micro-capacitance sensor
Energy storage Optimal Scheduling does not account for the collaboration optimization side of direct current and the electricity consumption type and DC-AC conversion device that exchange mixing
Method.
Invention content
In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a kind of electricity consumption classes for considering direct current and exchanging mixing
The micro-capacitance sensor energy storage Optimal Scheduling cooperative optimization method of type and DC-AC conversion device.This method be based on load, photovoltaic and
The prediction result and electricity price of wind turbine power generation to the charging and discharging state of the transition status of AC/DC changeover switch and energy-storage battery with
The minimum target of total electricity bill optimizes.Rational optimization is carried out by the electric energy to micro-capacitance sensor energy storage Optimal Scheduling to make
The load of electricity price peak time is transferred to electricity price low ebb period by total system, to achieve the purpose that power cost saving, this method can
To apply in different types of intelligent micro-grid energy storage Optimal Scheduling, whether there is or not electrothermal load and alternating current-direct current loads.
The technical solution adopted in the present invention is：
To including that major network supplies electricity to ac bus, ac bus is turned with DC bus by alternating current-direct current bidirectional transducer
It changes, is furnished with AC load on ac bus, is furnished with DC load, energy-storage battery, photovoltaic power generation apparatus and wind on DC bus
The typical intelligent micro-grid structure of machine power generator optimizes scheduling,
The dispatching method includes the following steps：
Step 1：Load and photovoltaic, wind turbine power generation to next day are predicted.
Step 2：Based on load and photovoltaic, the prediction result of wind turbine power generation and electricity price to the conversion shape of AC/DC changeover switch
The charging and discharging state of state and energy-storage battery establishes Optimized model with total electricity bill minimum.
Step 3：Optimized model is optimized.
For the step 1, prediction technique is common customer charge and photovoltaic, wind turbine power generation prediction technique, such as nerve
Network and support vector machine method.
For the step 2, the object function of optimization constrains for whole day total electricity bill.
In formula, t converts for the period, and 96 be whole day period sum, and whole day period sum may be other suitable values.C
(t) tou power price for being period t.P (t) is obtained output powers of the period t from major network, and T is unit Period Length, herein
For 0.25h.
Constraints includes,
General power Constraints of Equilibrium：
P (t)=PAC-load(t)+PAC-DC(t)
In formula, PAC-load(t) AC load for being period t, PAC-DC(t) it is the power of AC/DC changeover switch.
AC/DC changeover switch efficiency constraints：
In formula, ηA/DThe transfer efficiency of direct current, η are converted to for exchangeD/AThe transfer efficiency of exchange, P are converted into for direct currentDC
(t) the DC bus total load for being period t.
DC bus total load constrains：
PDC(t)+PPV(t)+Pw(t)=PDC-load(t)+PB(t)
In formula, PPV(t) it is photovoltaic generation power, Pw(t) it is wind-power electricity generation power, PDC-load(t) it is DC load, PB(t)
It is energy-storage battery in the power of period t, P when chargingB(t) 0 ＞, P when electric dischargeB(t) 0 ＜.
Efficiency for charge-discharge constrains：
In formula,For maximum charge efficiency,For maximum discharging efficiency, PB(t) charge efficiency for being period t.
Energy-storage battery state of charge constrains：
Emin≤EB(t)≤Emax
In formula, EmaxFor energy-storage battery maximum electricity, EminFor the minimum electricity of energy-storage battery, Emin≤EB(t) it is period t's
Battery status.
Battery electric quantity state giant ties：
In formula, E (0) is battery initial quantity of electricity, and E (t) is the accumulation electricity of period t.
Day electricity giant ties：
The formula indicates that energy-storage battery day accumulation electricity is 0.
For the step 3, optimization process is：
(1) load and photovoltaic, wind turbine prediction and electricity price information are read
(2) model objective function and constraints are established
(3) model is optimized using branch and bound method
(4) optimum results are exported：The charge-discharge electric power of battery and period, the power of AC/DC changeover switch and period.
The beneficial effects of the invention are as follows relative to existing micro-capacitance sensor energy storage Optimized Operation optimization method, the present invention examines simultaneously
AC load and DC load are considered, the charging and discharging state of transition status and energy-storage battery to AC/DC changeover switch carries out
Optimization, and then realize that the purpose of power cost saving, the present invention are suitable for different types of micro-capacitance sensor user.
Description of the drawings
Fig. 1 is typical intelligent micro-grid structure chart；
Fig. 2 is micro-capacitance sensor energy storage Optimal Scheduling figure.
Specific implementation mode
The preferred embodiment that the invention will now be described in detail with reference to the accompanying drawings.
Embodiment supplies electricity to ac bus as shown in Figure 1, typical intelligent micro-grid structure includes major network, ac bus with it is straight
Stream busbar is converted by alternating current-direct current bidirectional transducer, is furnished with AC load on ac bus, is furnished with direct current on DC bus
Load, energy-storage battery, photovoltaic power generation apparatus and wind turbine power generation device.
First with common customer charge, photovoltaic and wind turbine prediction technique, the present embodiment using neural network and support to
Amount machine method predicts next day load and photovoltaic and wind-power electricity generation.
Load and photovoltaic, the prediction result of wind turbine power generation and electricity price are then based on to the transition status of AC/DC changeover switch
And the charging and discharging state of energy-storage battery establishes Optimized model with total electricity bill minimum.
The object function of optimization constrains for whole day total electricity bill.
In formula, t converts for the period, and 96 be whole day period sum, and whole day period sum may be other suitable values.C
(t) tou power price for being period t.P (t) is obtained output powers of the period t from major network, and T is unit Period Length, herein
For 0.25h.
Constraints includes：
General power Constraints of Equilibrium：
P (t)=PAC-load(t)+PAC-DC(t)
PAC-load(t) AC load for being period t, PAC-DC(t) it is the power of AC/DC changeover switch.
AC/DC changeover switch efficiency constraints：
In formula：ηA/DThe transfer efficiency of direct current, η are converted to for exchangeD/AThe transfer efficiency of exchange, P are converted into for direct currentDC
(t) the DC bus total load for being period t.
DC bus total load constrains：
PDC(t)+PPV(t)+Pw(t)=PDC-load(t)+PB(t)
PPV(t) it is photovoltaic generation power, Pw(t) it is wind-power electricity generation power, PDC-load(t) it is DC load, PB(t) it is storage
Can battery in the power of period t, P when chargingB(t) 0 ＞, P when electric dischargeB(t) 0 ＜.
Efficiency for charge-discharge constrains：
For maximum charge efficiency,For maximum discharging efficiency, PB(t) charge efficiency for being period t.
Energy-storage battery state of charge constrains：
Emin≤EB(t)≤Emax
EmaxFor energy-storage battery maximum electricity, EminFor the minimum electricity of energy-storage battery, Emin≤EB(t) the battery shape for being period t
State.
Battery electric quantity state giant ties：
E (0) is battery initial quantity of electricity, and E (t) is the accumulation electricity of period t.
Day electricity giant ties：
The formula indicates that energy-storage battery day accumulation electricity is 0.
Finally, Optimized model is optimized.
With reference to Fig. 2, optimization process is：
(1) load is read；
(2) photovoltaic, wind turbine prediction and electricity price information are read；
(3) model objective function and constraints are established；
(4) model is optimized using branch and bound method；
(5) optimum results are exported：The charge-discharge electric power of battery and period, the power of AC/DC changeover switch and period.
Claims (3)
1. a kind of micro-capacitance sensor energy storage Optimization Scheduling, to including direct current and the electricity consumption type and AC/DC changeover switch that exchange mixing
Intelligent micro-grid structure optimize scheduling, it is characterised in that the dispatching method includes the following steps：
Step 1 predicts next day load and photovoltaic, wind turbine power generation；
Step 2 based on load and photovoltaic, the prediction result of wind turbine power generation and electricity price to the transition status of AC/DC changeover switch with
And the charging and discharging state of energy-storage battery establishes Optimized model with total electricity bill minimum；
Step 3 optimizes Optimized model；
The object function optimized in the step 2 constrains for whole day total electricity bill
In formula, segment variable when t is, 96 be whole day period sum, and whole day period sum is 96 or is other suitable values, and C (t) is
The tou power price of period t, P (t) be obtained output powers of the period t from major network, T be unit Period Length, herein for
0.25h or other suitable values；
Constraints includes：
General power Constraints of Equilibrium
P (t)=PAC-load(t)+PAC-DC(t)
In formula, PAC-load(t) AC load for being period t, PAC-DC(t) it is the power of AC/DC changeover switch；
AC/DC changeover switch power constraint
In formula, ηA/DThe transfer efficiency of direct current, η are converted to for exchangeD/AThe transfer efficiency of exchange, P are converted into for direct currentDC(t) it is
The DC bus total load of period t；
DC bus total load constrains
PDC(t)+PPV(t)+Pw(t)=PDC-load(t)+PB(t)
In formula, PPV(t) it is photovoltaic generation power, Pw(t) it is wind-power electricity generation power, PDC-load(t) it is DC load, PB(t) it is storage
Can battery in the power of period t, P when chargingB(t) 0 ＞, P when electric dischargeB(t) 0 ＜；
Charge-discharge electric power constrains
In formula,For maximum charge power,For maximum discharge power, PB(t) charge power for being period t；
Energy-storage battery state of charge constrains
Emin≤EB(t)≤Emax
In formula, EmaxFor energy-storage battery maximum electricity, EminFor the minimum electricity of energy-storage battery, EB(t) battery status for being period t；
Battery electric quantity state giant ties
In formula, E (0) is battery initial quantity of electricity, and E (t) is the accumulation electricity of period t；
Day electricity giant ties
The formula indicates that energy-storage battery day accumulation electricity is 0.
2. a kind of micro-capacitance sensor energy storage Optimization Scheduling according to claim 1, it is characterised in that：It is pre- in the step 1
Survey method is neural network and support vector machine method.
3. a kind of micro-capacitance sensor energy storage Optimization Scheduling according to claim 1, it is characterised in that its in the step 3 is excellent
Change process is：
(1) load, photovoltaic and wind turbine prediction and electricity price information are read；
(2) model objective function and constraints are established；
(3) model is optimized using branch and bound method；
(4) optimum results are exported：The charge-discharge electric power of battery and period；The power of AC/DC changeover switch and period.
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| CN106779471B (en) * | 2017-01-05 | 2024-02-09 | 沈阳工业大学 | Multi-energy interconnected AC/DC hybrid micro-grid system and optimal configuration method |
| CN106709610B (en) * | 2017-01-12 | 2020-04-21 | 浙江大学 | Micro-grid electricity energy storage and ice storage combined optimization scheduling method |
| CN106684916B (en) * | 2017-02-16 | 2019-04-09 | 上海电力学院 | A kind of grid-connected photovoltaic system running optimizatin method with battery |
| CN107069791B (en) * | 2017-06-16 | 2019-07-16 | 浙江大学 | A kind of integration requirement response method for considering industrial park and being interacted with factory |
| CN110224420A (en) * | 2019-06-12 | 2019-09-10 | 新奥数能科技有限公司 | The linearization technique and device of energy-storage system charging and recharging model |
| CN110460101A (en) * | 2019-09-05 | 2019-11-15 | 北京双登慧峰聚能科技有限公司 | Island microgrid energy storage subsystem and control method |
| CN112436555A (en) * | 2020-12-02 | 2021-03-02 | 中国华能集团有限公司 | Shared energy storage system and method based on block chain technology |
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