CN104682436A - Energy storage system micro-grid capable of inhibiting power fluctuation - Google Patents
Energy storage system micro-grid capable of inhibiting power fluctuation Download PDFInfo
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- 238000004146 energy storage Methods 0.000 title claims abstract description 47
- 230000002401 inhibitory effect Effects 0.000 title abstract 2
- 238000010248 power generation Methods 0.000 claims abstract description 48
- 230000008859 change Effects 0.000 claims abstract description 11
- 238000012544 monitoring process Methods 0.000 claims description 40
- 238000011156 evaluation Methods 0.000 claims description 36
- 238000000034 method Methods 0.000 claims description 14
- 230000000087 stabilizing effect Effects 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 4
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 238000007599 discharging Methods 0.000 claims description 3
- 229910052744 lithium Inorganic materials 0.000 claims description 3
- 230000002441 reversible effect Effects 0.000 claims description 3
- 238000002955 isolation Methods 0.000 claims description 2
- 238000006243 chemical reaction Methods 0.000 abstract description 12
- 230000008901 benefit Effects 0.000 abstract description 3
- 238000011217 control strategy Methods 0.000 abstract description 3
- 230000002457 bidirectional effect Effects 0.000 abstract 4
- 238000012806 monitoring device Methods 0.000 abstract 1
- 230000002035 prolonged effect Effects 0.000 abstract 1
- 230000005611 electricity Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000005855 radiation Effects 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000013277 forecasting method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
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- 210000000352 storage cell Anatomy 0.000 description 1
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Classifications
-
- 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
-
- 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
-
- H02J3/386—
-
- 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 invention discloses an energy storage system micro-grid 10 capable of inhibiting power fluctuation. The micro-grid 10 comprises photovoltaic power generation equipment 12, an energy storage system 13, wind power generation equipment 14, an AC/DC bidirectional current conversion module I 16, a direct-current bus, an AC/DC bidirectional current conversion module II 15, a load 17 and a monitoring device 11, wherein the AC/DC bidirectional current conversion module I 16 is used for connecting and isolating the photovoltaic power generation equipment 12 from the direct-current bus; the AC/DC bidirectional current conversion module II 15 is used for connecting the photovoltaic power generation equipment 12 with the direct-current bus. The micro-grid disclosed by the invention has the advantages that firstly, the output power change situation of the wind power generation equipment and the photovoltaic power generation equipment can be accurately predicted, secondly, the power change of a connecting point of the micro-grid with a large power grid and the power change of the load inside the micro-grid can be accurately predicted, and thirdly, large power grid scheduling requirements and the operation situation of the energy storage system are both considered in a control strategy, active power and reactive power can be simultaneously provided for the large power grid, the scheduling requirements of the large power grid and the requirements of the load inside the micro-grid are met, at the same time the power fluctuation of the micro-grid can be effectively inhibited, the power supply reliability is also achieved, the security of the micro-grid is ensured, and the service life of equipment inside the micro-grid is prolonged.
Description
Art
The present invention relates to a kind of micro-capacitance sensor with the energy-storage system can stabilizing power fluctuation.
Background technology
Micro-capacitance sensor (Micro-Grid) is also translated into microgrid; it is a kind of new network structure; it is the system unit that a group of micro battery, load, energy-storage system and control device are formed; can teaching display stand control, the autonomous system of protect and manage; both can be incorporated into the power networks with external electrical network, also can isolated operation.
Using the micro-capacitance sensor of wind-powered electricity generation and photovoltaic generation as superhigh pressure, the supplementing of remote, bulk power grid powering mode, represent the developing direction that electric power system is new.Wind energy and solar energy resources are clean regenerative resources, but there is the problem of randomness and fluctuation, bring a series of impact to electrical network.The original trend distribution of the fluctuation degree direct influence electrical network of power, when the permeability of wind power generation and photovoltaic generation is in higher level, fluctuation and randomness bring huge impact can to original operational mode of electrical network.In order to reduce this impact, large-scale energy storage system cooperation can be configured in the system of Wind turbines and photovoltaic plant cogeneration.
The realization of energy storage technology to micro-capacitance sensor plays an important role, and it is applied in the fluctuation and stochastic problems that solve generation of electricity by new energy to a great extent, effectively improves the predictability in intermittent micro-source, certainty and economy.In addition, energy storage technology is at frequency modulation and voltage modulation and improvement system is meritorious, reactive balance level, and the effect improving micro-capacitance sensor stable operation ability aspect also obtain extensively research and proof.
But, now configure large-scale energy storage system price comparison expensive, therefore, be necessary to consider power transmission engineering cost, energy-storage system cost, transmission of electricity income, energy-storage system income, set up and turn to target so that comprehensive benefit is maximum, the method that energy-storage system during given transmission line ability to transmit electricity is distributed rationally.
Summary of the invention
The invention provides a kind of micro-capacitance sensor with the energy-storage system can stabilizing power fluctuation, load variations in the generated output of the generating equipment in the measurable micro-capacitance sensor of this micro-capacitance sensor and micro-capacitance sensor, traceable and prediction micro-capacitance sensor and bulk power grid tie point power, the battery module battery capacity of real-time detection, can formulate and implement optimum control strategy, ensure that micro-capacitance sensor steadily provides active power and reactive power according to the demand of bulk power grid when grid-connected, and promote fail safe and the useful life of energy-storage system.
To achieve these goals, the invention provides a kind of micro-capacitance sensor with the energy-storage system can stabilizing power fluctuation, this micro-capacitance sensor comprises: wind power plant, photovoltaic power generation equipment, energy-storage system, AC/DC two-way change of current module one, DC bus, the two-way change of current module two of AC/DC being used for connecting wind power plant, photovoltaic power generation equipment and DC bus, micro-capacitance sensor internal burden and supervising device for being connected with bulk power grid by micro-capacitance sensor and isolating;
The two-way DC/DC converter that this energy-storage system comprises battery module, is connected with above-mentioned DC bus;
This supervising device comprises:
Wind power generation generating equipment monitoring module, for monitoring wind power plant in real time, and predicts the generated output of wind power plant;
Photovoltaic power generation equipment monitoring module, for monitoring photovoltaic power generation equipment in real time, and predicts the generated output of photovoltaic power generation equipment;
Energy-storage system monitoring module, can monitor SOC and the DC/DC reversible transducer of battery module in real time;
Bulk power grid contact module, knows the ruuning situation of bulk power grid and relevant schedule information for real-time from bulk power grid regulation and control center;
Be incorporated into the power networks monitoring module, connects or isolation bulk power grid for controlling micro-capacitance sensor;
Load monitoring module, for monitoring the load in energy-accumulating power station in real time;
Middle control module, for determining the operation reserve of micro-capacitance sensor, and sends instruction to each module in above-mentioned supervising device, to perform this operation reserve;
Bus module, for the liaison of the modules of this supervising device.
Preferably, photovoltaic power generation equipment monitoring module at least comprises photovoltaic power generation equipment voltage, current detecting equipment, light intensity and temperature testing equipment.
Preferably, the service data of described photovoltaic power generation equipment monitoring module Real-time Obtaining photovoltaic power generation equipment, and store data.
Preferably, described wind power plant monitoring module at least comprises wind power plant voltage, electric current and frequency detection equipment, wind speed measurement equipment.
Preferably, the service data of described wind power plant monitoring module Real-time Obtaining wind power plant, and store data.
Preferably, energy-storage system monitoring module at least comprises accumulator voltage, electric current, SOC acquisition equipment and temperature testing equipment.
Preferably, described SOC obtains equipment and comprises: the first acquisition module, for obtaining the operating state of battery; First determination module, for determining the evaluation method of estimating battery state-of-charge according to the operating state of battery; Computing module, for being in the battery charge state value under different operating states according to evaluation method calculating battery.
Preferably, the first determination module comprises: first determines submodule, and for when the operating state got is inactive state, determine that evaluation method is the first evaluation method, wherein, the first evaluation method comprises open circuit voltage method; Second determines submodule, for when the operating state got is for returning to form, determines that evaluation method is the second evaluation method; 3rd determines submodule, and for when the operating state got is charging and discharging state, determine that evaluation method is the 3rd evaluation method, wherein, the 3rd evaluation method comprises Kalman filtering method.
Preferably, battery module adopts lithium battery as the base unit of power storage.
Preferably, described battery module, comprises n battery pack, described two-way DC/DC converter has n DC/DC current transformer, n is more than or equal to 3, and each battery pack is by the discharge and recharge of a DC/DC inverter controller, and this n DC/DC current transformer controls by energy-storage system monitoring module.
Micro-capacitance sensor tool of the present invention has the following advantages: the power output situation of change of (1) Accurate Prediction wind power plant and photovoltaic power generation equipment; (2) changed power of Accurate Prediction micro-capacitance sensor and bulk power grid tie point and the changed power of micro-capacitance sensor internal load; (3) control strategy is taken into account and is joined bulk power grid scheduling requirement and energy-storage system ruuning situation, can simultaneously for bulk power grid provides active power and reactive power, while the dispatching requirement meeting bulk power grid and micro-capacitance sensor internal load demand, effectively can suppress the power fluctuation of micro-capacitance sensor, take into account power supply reliability, ensure the fail safe of micro-capacitance sensor, extend the useful life of equipment in micro-capacitance sensor.
Accompanying drawing explanation
Fig. 1 shows of the present inventionly a kind ofly has the micro-capacitance sensor of the energy-storage system can stabilizing power fluctuation and the block diagram of supervising device thereof;
Fig. 2 shows operation and the method for supervising of micro-capacitance sensor of the present invention.
Embodiment
Fig. 1 shows a kind of micro-capacitance sensor 10 with the energy-storage system can stabilizing power fluctuation of the present invention, and this micro-capacitance sensor 10 comprises: photovoltaic power generation equipment 12, energy-storage system 13, wind power plant 14, AC/DC two-way change of current module 1 for micro-capacitance sensor 10 and bulk power grid 20 are connected and are isolated, DC bus, the two-way change of current module 2 15 of AC/DC being used for connecting photovoltaic power generation equipment 12 and DC bus, load 17 and supervising device 11.
See Fig. 1, the two-way DC/DC converter 132 that this energy-storage system 13 comprises battery module 131, is connected with above-mentioned DC bus.
This supervising device 11 comprises: photovoltaic power generation equipment monitoring module 114, for the photovoltaic power generation equipment 12 in real-time monitoring battery energy-storage system 10, and predicts the generated output of photovoltaic power generation equipment 12; Energy-storage system monitoring module 115, for monitoring battery module 131 in energy-storage system 131 and DC/DC bidrectional transducer 132 in real time; Bulk power grid contact module 112, regulates and controls center from bulk power grid 20 know the ruuning situation of bulk power grid 20 and relevant schedule information for real-time; Parallel control module 116, connects or isolates bulk power grid 20 for controlling micro-capacitance sensor 10; Middle control module 117, for determining the operation reserve of micro-capacitance sensor 10, and sends instruction to above-mentioned each module, to perform this power supply strategy; Wind power plant monitoring module 113, for monitoring wind power plant 14 in real time; Load monitoring module 118, for the load 17 in real-time micro-capacitance sensor 10; Bus module 111, for the liaison of the modules of this supervising device 11.
Communication module 111, for the communication between above-mentioned modules, described bus communication module 111 is connected with other modules by redundancy dual CAN bus.
Photovoltaic power generation equipment 12 comprises multiple photovoltaic generating module, and photovoltaic power generation equipment monitoring module 114 at least comprises voltage, electric current, frequency detection equipment, the light-intensity test equipment of photovoltaic power generation equipment.
The service data of described wind power plant monitoring module 113 Real-time Obtaining wind power plant 12, and store data.
Energy-storage system monitoring module 116 at least comprises accumulator voltage, electric current, SOC acquisition equipment and temperature testing equipment, can monitor the SOC of battery module in real time.
Described SOC obtains equipment and comprises: the first acquisition module, for obtaining the operating state of battery; First determination module, for determining the evaluation method of estimating battery state-of-charge according to the operating state of battery; Computing module, for being in the battery charge state value under different operating states according to evaluation method calculating battery.
First determination module comprises: first determines submodule, and for when the operating state got is inactive state, determine that evaluation method is the first evaluation method, wherein, the first evaluation method comprises open circuit voltage method; Second determines submodule, for when the operating state got is for returning to form, determines that evaluation method is the second evaluation method; 3rd determines submodule, and for when the operating state got is charging and discharging state, determine that evaluation method is the 3rd evaluation method, wherein, the 3rd evaluation method comprises Kalman filtering method.
Further, evaluation method is the 3rd evaluation method, and computing module comprises: set up module, for the battery model utilizing three rank equivalent electric circuits to set up battery; Second determination module, for determining the state equation of battery model and measuring equation; First calculating sub module, for using state equation and the battery charge state value measuring equation calculating battery.
Further, evaluation method is the second evaluation method, and computing module comprises: the second acquisition module, is entering the operating state before returning to form for obtaining battery; Second calculating sub module, at battery when entering the operating state before returning to form and being discharge condition, according to the first formulae discovery battery charge state value, wherein, the first formula is
sOC
tfor the battery charge state value under returning to form, SOC
dfor battery charge state value when discharge condition stops, M is the accumulation electricity in battery discharge procedure, t be battery in the time returning to form lower experience, h is the default duration returned to form, and Q is the actual capacity of battery; 3rd calculating sub module, at battery when entering the operating state before returning to form and being charged state, according to the second formulae discovery battery charge state value, wherein, the second formula is SOC
t=SOC
c+ M × h × 100%, SOC
cfor battery charge state value when charged state stops.
Further, evaluation method is the first evaluation method, and computing module comprises: the 3rd acquisition module, for obtaining the open circuit voltage of battery; Read module, for reading battery charge state value corresponding to open circuit voltage.
Preferably, battery module 131 adopts lithium battery as the base unit of power storage.
Preferably, described battery module 131, comprises n battery pack, described DC/DC reversible transducer 132 has n DC/DC current transformer, n is more than or equal to 3, and each battery pack is by the discharge and recharge of a DC/DC inverter controller, and this n DC/DC current transformer controls by energy-storage system monitoring module.
Middle control module 117 at least comprises CPU element, data storage cell and display unit.
Bulk power grid contact module 112 at least comprises Wireless Telecom Equipment.
Parallel control module 116 at least comprises checkout equipment, data acquisition unit and data processing unit for detecting bulk power grid 20 and micro-capacitance sensor 10 voltage, electric current and frequency.Data acquisition unit comprises collection preliminary treatment and A/D modular converter, gathers eight tunnel telemetered signal amounts, comprises grid side A phase voltage, electric current, the three-phase voltage of energy-accumulating power station side, electric current.Remote measurement amount changes strong ac signal (5A/110V) into inner weak electric signal without distortion by the high-precision current in terminal and voltage transformer, after filtering process, enter A/D chip carry out analog-to-digital conversion, digital signal after conversion calculates through data processing unit, obtains three-phase voltage current value and the bulk power grid 20 side phase voltage current value of wind energy turbine set energy-storage system 10 side.The process of this telemetered signal amount have employed high-speed and high-density synchronized sampling, automatic frequency tracking technology also has the fft algorithm improved, so precision is fully guaranteed, the measurement and process that gain merit in wind energy turbine set energy-storage system 10 side, idle and electric energy is from first-harmonic to higher harmonic components can be completed.
See accompanying drawing 2, method of the present invention comprises the steps:
S1. the service data of wind power plant and photovoltaic power generation equipment monitoring module Real-time Obtaining wind power plant and photovoltaic power generation equipment, and store data;
S2. according to the service data of wind power plant and photovoltaic power generation equipment, the power output of the wind power plant in following predetermined instant and photovoltaic power generation equipment is predicted;
S3. the SOC obtaining battery module is detected in real time, Real-time Obtaining micro-capacitance sensor internal burden power demand conditions;
S4. the parameter of Real-time Obtaining bulk power grid and schedule information, the power demand of prediction future time micro-capacitance sensor and bulk power grid tie point;
S5. the power demand of energy-accumulating power station and bulk power grid tie point, current batteries to store energy SOC, current be electrical network internal burden power demand, following wind power plant and photovoltaic power generation equipment power output as constraints, realize the optimizing operation of micro-capacitance sensor.
Preferably, the power output of arbitrary wind-power generated power forecasting method prediction wind power plant in prior art is adopted in step s 2.
Preferably, photovoltaic power generation equipment comprises photovoltaic module, in step s 2 described, predicts the power output of photovoltaic power generation equipment in the following way:
S21. the model of exerting oneself of photovoltaic module is set up: P
pv(t)=η
invη
pv(t) G (t) S
pv(1)
S in formula
pvfor photovoltaic panel receives the area (m of solar irradiation radiation
2), G (t) light radiation numerical value (W/m
2), η
pvt () is photovoltaic module energy conversion efficiency, η
invfor inverter conversion efficiency;
Wherein, the energy conversion efficiency of photovoltaic module is relevant with the temperature of environment, and ambient temperature on the impact of photovoltaic module energy conversion efficiency is:
η in formula
rfor the reference energy conversion efficiency of testing under photovoltaic module normal temperature, β is the influence coefficient of temperature to energy conversion efficiency, T
ct () is the temperature value of t photovoltaic module, T
crfor photovoltaic module normative reference temperature value; Photovoltaic module absorbs solar radiation, and can work with ambient temperature one and cause photovoltaic module temperature to change, its expression formula is as follows:
In formula, T is the ambient temperature of surrounding, T
ratthe rated temperature that photovoltaic module runs;
S22. detect in real time and the information and ambient temperature at sunshine of periphery of collection photovoltaics assembly, according to history information at sunshine and ambient temperature, the intensity of sunshine in prediction a period of time in future and ambient temperature;
S23. according to the intensity of sunshine in following a period of time and ambient temperature, the model of exerting oneself of above-mentioned photovoltaic module is utilized to calculate the generated output of the photovoltaic power generation equipment in future time.
Preferably, in step s 4 which, following steps are adopted to realize tracking and the prediction of micro-capacitance sensor and bulk power grid tie point place power demand:
S41. specify micro-capacitance sensor power positive direction everywhere, it is just that power direction flows to bulk power grid with micro-capacitance sensor;
S42. expect according to the actual power of micro-capacitance sensor each point and the power of points of common connection the power calculating micro-grid system points of common connection place, computing formula is:
P in formula
ifor being the total generated power forecasting value of scene, P
i_Sfor energy-storage system is to the power output of bulk power grid, P
pCCfor points of common connection is to the power output of bulk power grid, P
loadfor flowing into the power of micro-capacitance sensor internal burden;
S43. P is determined
pCCspan: P
pCC min≤ P
pCC≤ P
pCC max, the power of points of common connection now can be made to remain within the scope of the acceptable trend of distribution, P
pCC minand P
pCC maxfor the minimum gate threshold value that obtained by distribution Load flow calculation and maximum threshold value, work as P
pCCfluctuation when exceeding above-mentioned restriction threshold, need the power output of the energy-storage travelling wave tube regulated in microgrid to stabilize the power at microgrid points of common connection place.
Preferably, optimizing operation is realized in the following way in step s 5:
S51. obtain the first data acquisition system be made up of Wind power forecasting value and the second data acquisition system be made up of photovoltaic power generation power prediction value respectively, after the Wind power forecasting value in described first data acquisition system being added with the photovoltaic power generation power prediction value in described second data acquisition system, obtain the integrated data set be made up of the total generated power forecasting value of scene;
S52. fitting of a polynomial algorithm is utilized to carry out matching to described integrated data set, formula of smoothly being exerted oneself;
S53. smoothly to exert oneself output valve according to described formulae discovery of smoothly exerting oneself;
S54. according to magnitude relationship and the absolute difference of described smoothly exert oneself output valve and the total generated power forecasting value of described scene, mode of exerting oneself and the power stage value of energy-storage system is determined;
Described step S51 specifically comprises:
Obtain the first data acquisition system P be made up of Wind power forecasting value
1:
P
1＝{(p
1i,t
i)|i＝1,2...,m}； (5)
Obtain the second data acquisition system P be made up of photovoltaic power generation power prediction value
2:
P
2＝{(p
2i,t
i)|i＝1,2...,m}； (6)
By described first data acquisition system P
1in Wind power forecasting value and described second data acquisition system P
2in photovoltaic power generation power prediction value be added after obtain the integrated data set P that is made up of the total generated power forecasting value of scene:
P＝{(p
i,t
i)|i＝1,2...,m}； (7)
Wherein, p
i=p
1i+ p
2i;
Wherein, P
1be the first data acquisition system, p
1ifor Wind power forecasting value, P
2be the second data acquisition system, p
2ifor Wind power forecasting value, P is integrated data set, p
ifor the total generated power forecasting value of scene, m is the number of samples of the first data acquisition system, the second data acquisition system, the 3rd data acquisition system, and m is natural number, and i is sample sequence number, t
ifor p
1i, p
2i, p
ithe corresponding time;
Described step S52 specifically comprises:
S521. according to the total generated power forecasting value p of scene in described integrated data set P
ifluctuation tendency, determine the exponent number n of described formula of smoothly exerting oneself, wherein n is natural number;
S522. matching has the multinomial of described exponent number n:
a
nt
i n+a
n-1t
i n-1+…+a
1t
i+a
0； (8)
Wherein, a
0~ a
nfor multinomial coefficient;
Step B3, calculates described multinomial a
nt
i n+ a
n-1t
i n-1+ ... + a
1t
i+ a
0generated power forecasting value p total with described scene
isquared difference and Err:
When S522. utilizing least square method to calculate described squared difference and Err for minimum value, multinomial coefficient a
0~ a
ncorresponding occurrence α
0~ α
n;
S523. described occurrence α is utilized
0~ α
nbuild formula X (t) of smoothly exerting oneself:
X(t)＝α
nt
n+α
n-1t
n-1+…+α
1t+α
0； (10)
Wherein, t is the time;
Described S53 is specially:
Calculate and work as t=t
itime, the value X (t of described formula X (t) of smoothly exerting oneself
i):
X(t
i)＝α
nt
i n+α
n-1t
i n-1+…+α
1t
i+α
0(11)
Wherein, X (t
i) be output valve of smoothly exerting oneself;
Described step S54 specifically comprises:
As the described output valve X (t that smoothly exerts oneself
i) be greater than the total generated power forecasting value p of described scene
itime, energy-storage system release electric energy, and power stage value is:
p′
i＝X(t
i)-p
i＝(α
nt
i n+α
n-1t
i n-1+…+α
1t
i+α
0)-p
i(12)
Wherein, p '
ifor t
ithe power stage value of moment energy-storage system;
As the described output valve X (t that smoothly exerts oneself
i) be less than the total generated power forecasting value p of described scene
itime, energy-storage system absorbs electric energy, and power stage value is:
p′
i＝p
i-X(t
i)＝p
i-(α
nt
i n+α
n-1t
i n-1+…+α
1t
i+α
0)； (13)
As the described output valve X (t that smoothly exerts oneself
i) equal the total generated power forecasting value p of described scene
itime, energy-storage system power stage value is zero.
Above content is in conjunction with concrete preferred implementation further description made for the present invention, can not assert that specific embodiment of the invention is confined to these explanations.For general technical staff of the technical field of the invention, without departing from the inventive concept of the premise, make some equivalent to substitute or obvious modification, and performance or purposes identical, all should be considered as belonging to protection scope of the present invention.
Claims (10)
1. have a micro-capacitance sensor for the energy-storage system can stabilizing power fluctuation, this micro-capacitance sensor comprises: wind power plant, photovoltaic power generation equipment, energy-storage system, AC/DC two-way change of current module one, DC bus, the two-way change of current module two of AC/DC being used for connecting wind power plant, photovoltaic power generation equipment and DC bus, micro-capacitance sensor internal burden and supervising device for being connected with bulk power grid by micro-capacitance sensor and isolating;
The two-way DC/DC converter that this energy-storage system comprises battery module, is connected with above-mentioned DC bus;
This supervising device comprises:
Wind power generation generating equipment monitoring module, for monitoring wind power plant in real time, and predicts the generated output of wind power plant;
Photovoltaic power generation equipment monitoring module, for monitoring photovoltaic power generation equipment in real time, and predicts the generated output of photovoltaic power generation equipment;
Energy-storage system monitoring module, can monitor SOC and the DC/DC reversible transducer of battery module in real time;
Bulk power grid contact module, knows the ruuning situation of bulk power grid and relevant schedule information for real-time from bulk power grid regulation and control center;
Be incorporated into the power networks monitoring module, connects or isolation bulk power grid for controlling micro-capacitance sensor;
Load monitoring module, for monitoring the load in energy-accumulating power station in real time;
Middle control module, for determining the operation reserve of micro-capacitance sensor, and sends instruction to each module in above-mentioned supervising device, to perform this operation reserve;
Bus module, for the liaison of the modules of this supervising device.
2. micro-capacitance sensor as claimed in claim 1, it is characterized in that, photovoltaic power generation equipment monitoring module at least comprises photovoltaic power generation equipment voltage, current detecting equipment, light intensity and temperature testing equipment.
3. micro-capacitance sensor as claimed in claim 2, is characterized in that, the service data of described photovoltaic power generation equipment monitoring module Real-time Obtaining photovoltaic power generation equipment, and stores data.
4. micro-capacitance sensor as claimed in claim 3, it is characterized in that, described wind power plant monitoring module at least comprises wind power plant voltage, electric current and frequency detection equipment, wind speed measurement equipment.
5. micro-capacitance sensor as claimed in claim 4, is characterized in that, the service data of described wind power plant monitoring module Real-time Obtaining wind power plant, and stores data.
6. micro-capacitance sensor as claimed in claim 5, is characterized in that, energy-storage system monitoring module at least comprises accumulator voltage, electric current, SOC acquisition equipment and temperature testing equipment.
7. micro-capacitance sensor as claimed in claim 6, is characterized in that, described SOC obtains equipment and comprises: the first acquisition module, for obtaining the operating state of battery; First determination module, for determining the evaluation method of estimating battery state-of-charge according to the operating state of battery; Computing module, for being in the battery charge state value under different operating states according to evaluation method calculating battery.
8. micro-capacitance sensor as claimed in claim 7, it is characterized in that, the first determination module comprises: first determines submodule, for when the operating state got is inactive state, determine that evaluation method is the first evaluation method, wherein, the first evaluation method comprises open circuit voltage method; Second determines submodule, for when the operating state got is for returning to form, determines that evaluation method is the second evaluation method; 3rd determines submodule, and for when the operating state got is charging and discharging state, determine that evaluation method is the 3rd evaluation method, wherein, the 3rd evaluation method comprises Kalman filtering method.
9. micro-capacitance sensor as claimed in claim 8, is characterized in that, battery module adopts lithium battery as the base unit of power storage.
10. micro-capacitance sensor as claimed in claim 9, it is characterized in that, described battery module, comprise n battery pack, described two-way DC/DC converter has n DC/DC current transformer, n is more than or equal to 3, and each battery pack is by the discharge and recharge of a DC/DC inverter controller, and this n DC/DC current transformer controls by energy-storage system monitoring module.
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