CN106877409B - Turn gas energy storage technology by electricity and improves electric system to the method for wind electricity digestion capability - Google Patents
Turn gas energy storage technology by electricity and improves electric system to the method for wind electricity digestion capability Download PDFInfo
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- 230000005611 electricity Effects 0.000 title claims abstract description 65
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Abstract
The raising electric system of gas energy storage technology is turned to the method for wind electricity digestion capability by electricity the invention discloses a kind of, including predict t period system loading a few days ago and a few days ago t period wind power output, by " ensure system to prediction gained wind-powered electricity generation by minimum abandonment in a manner of dissolve " for principle, the scheduling scheme I of setting optimization wind electricity digestion capability judges whether to need to translate load and participates in, solves translatable load, judging whether to need electricity to turn gas energy storage device to work and start electrical energy storage step.The present invention can turn device of air by electricity when wind power output is excessive and dissolve a part of wind-powered electricity generation, reduce the impact to electric system, its obtained artificial natural gas directly can be stored and be transported in natural gas system, and the demand to energy-storage battery is reduced, and reduce energy storage cost.On the other hand, natural gas can supply a part of thermic load, reduce the pressure of steam power plant.
Description
Technical field
The present invention relates to a kind of raising electric system to turn gas by electricity to the method for wind electricity digestion capability, more particularly to one kind
Energy storage technology improves electric system to the method for wind electricity digestion capability, belongs to field of new energy technologies.
Background technique
Large-scale wind power integration is one of the principal mode for developing new energy, is the important set of China's new energy development strategy
At part.With the rapid growth of China's installed capacity of wind-driven power, it is more and more prominent that large-scale wind power integration dissolves problem.Wind-powered electricity generation sheet
The randomness and fluctuation of body do not reach foot in forecasting wind speed so that the power output of wind power plant has very strong randomness
Under conditions of enough precision, the wind changed power and prediction error of hour grade mean to cause electric system very big impact.This
Outside, THE WIND ENERGY RESOURCES IN CHINA galore offset farther out from load center, large-scale wind power generation can not on-site elimination, need by defeated
Power grid remote conveying is sent to load center.A large amount of wind power long-distance sand transports often will cause system voltage and significantly change, office
The voltage stability of portion's power grid is affected, stability margin reduces.
In order to improve electric system to the digestion capability of wind-powered electricity generation, notification number is the Chinese invention patent of CN105656025A,
Disclose a kind of method for improving wind electricity digestion capability.This method is to predict second day wind power output and load data
On the basis of, abandonment appearance is judged whether there is, to control translation load, operation plan a few days ago is generated, to a certain degree
On the usage amount of wind-powered electricity generation can be improved.But the fluctuation and randomness due to wind energy are strong, are difficult to realize precisely prediction, work as wind
When fast excessive suddenly, just have a large amount of abandonment or occur cutting machine.Liu Xinfang et al. in electric power system protection and control,
2012,40 (6): it is disclosed in 35-39. " improving the theoretical research of large-scale wind power digestion capability using reasonable abandonment " a kind of logical
The method that the part wind-powered electricity generation power generation capacity future high extensive wind electricity digestion capability is abandoned in reasonable abandonment is crossed, is distributed using weibull
It is fitted equivalent load curve and as analysis foundation, quantization abandonment power, which generates electricity to the improvement coefficient of peak regulation and abandonment to wind-powered electricity generation, to be held
The influence of amount.This method can improve large-scale wind power digestion capability to a certain extent, however be to sacrifice a part of wind energy
For cost.
Energy-storage system can effectively inhibit wind power fluctuation, smooth output voltage, improve power quality, be to guarantee wind-force
Electricity generation grid-connecting operation, promotes the key technology of wind energy utilization.King is rushed et al. again in renewable energy, 2014,32 (7): 954-
One kind is disclosed at the beginning of Wind Power Generation in 960. " improving the power source planning research of wind electricity digestion capability based on energy-accumulating power station ", from
Planning level optimizes regional power system power supply architecture using energy-accumulating power station, to improve power grid to the side of the digestion capability of wind-powered electricity generation
Method.After comprehensively considering environmental benefit, social benefit, the market competitiveness, put into most with wind electricity digestion capability maximum and national economy
It is small be objective function, construct comprising thermoelectricity, energy-accumulating power station, large-scale wind power Generation Expansion Planning Model.Lu Qiuyu etc. is in Guangdong
Electric power, 2015,28 (12): 19-24. " the energy storage Optimal Configuration Method for improving wind electricity digestion capability " discloses one kind and comprehensively considers
The energy storage Stochastic Programming Model of wind storage system, conventional power unit and power grid correlation, proposing with abandonment ratio is constraint, investment
The energy storage power and capacity ratio optimization method of cost minimization.
The high cost of energy-storage system restricts its application at present, and using cost minimization as objective function, it is possible to make
At the too many problem of abandonment amount.And stored energy capacitance is limited, and when wind-powered electricity generation wide fluctuations, energy storage does not have sufficiently large capacity empty
Between, in addition, wind power output lesser season, stored energy capacitance configuration may be superfluous, and energy-storage system is not fully utilized.
Summary of the invention
The raising electric system of gas energy storage technology is turned to wind-powered electricity generation by electricity the technical problem to be solved in the present invention is to provide a kind of
The method of digestion capability.
In order to solve the above technical problems, the technical solution adopted by the present invention is that:
A method of gas energy storage technology is turned by electricity and improves electric system to wind electricity digestion capability, comprising the following steps:
Step 1: predicting t period system loading P a few days agoLOAD,tT period wind power output a few days agoT ∈ 1 ..., T,
1-T is 24 periods of next day: it is made of step in detail below:
Step 1-1: carrying out frequency domain decomposition for the actual measurement wind power output data of past one month wind power plant, obtains frequency diurnal periodicity
Domain component, low frequency component and high fdrequency component；
Step 1-2: frequency domain components diurnal periodicity prediction model is fitted using BP neural network；
Step 1-3: low frequency component prediction model is predicted using one-variable linear regression method:
In formula:
--- t period wind power output is predicted a few days ago；
--- known t1The practical power output of period wind-powered electricity generation；
--- known t2The practical power output of period wind-powered electricity generation；
Step 1-4: high fdrequency component is decomposed by Lifting Wavelet, the low frequency coefficient in obtained second layer Lifting Wavelet domain is used
BP neural network trains high fdrequency component mathematical model；
Step 1-5: frequency domain components diurnal periodicity prediction model, low frequency component prediction model and high fdrequency component mathematics are used respectively
Model prediction a few days ago wind power output diurnal periodicity frequency domain components predicted value, low frequency component predicted value and high fdrequency component predicted value；
Step 1-6: by wind power output a few days ago diurnal periodicity frequency domain components predicted value, low frequency component predicted value and high fdrequency component
Predicted value is cumulative, obtains the predicted value of wind power output a few days ago；
Step 1-7: predicting load by artificial neural network method: constructing the network structure of load forecasting model,
The history daily load for choosing two weeks is trained the coefficient of load forecasting model, meets it preset as training sample
Required precision；
Step 2: by " ensure system to prediction gained wind-powered electricity generation by minimum abandonment in a manner of dissolve " for principle, optimize wind electricity digestion
Ability:
Objective function are as follows:
In formula:
--- t period wind power output is predicted a few days ago；
PWIND,t--- the wind power output that system can be dissolved in the t period；
PLOAD,t--- t period system loading is predicted a few days ago；
di--- conventional power unit open state D={ di| i ∈ 1 ... N }, single unit all the period of time start and stop state constant, di
=1 i.e. unit booting, di=0 i.e. unit i is shut down；
PG,i,t--- conventional power unit i (including non-Wind turbines and sending interconnection equivalence unit outside) is in the power output of period t.
Objective function is accomplished that, arranges conventional power unit start and stop by planning a few days ago, guarantees system to the resulting wind of prediction
Electric consumption amount is maximum, i.e., abandonment amount is minimum.
Constraint condition are as follows:
The power balance of period t constrains:
Units limits of the conventional power unit in period t:
For non-Wind turbines,WithThe respectively minimum load and maximum output of unit；Interconnection is passed
Defeated power,WithRespectively send contracted period t minimum delivery electric power and maximum delivery electric power outside；
Conventional power unit increases and decreases units limits:
For non-Wind turbines,WithRespectively most increasing for unit contributes rate and maximum subtracts power output
Rate；For dominant eigenvalues,WithPower swing permissible value respectively under the constraint of interconnection appraisal standards
Bound；
Wind power output constraint:
Spinning reserve of the system in period t constrains:
In formulaRespectively meet the positive spare capacity of minimum and minimal negative spare capacity of system safety requirements；
Conventional power unit start and stop state constraint:
In formula: the non-quantity of state d for closing unitiIt is 1；When can close unit in booting or off-mode, state
Measure diAccordingly take 1 or 0；
Variable to be optimized is conventional power unit open state collection D=di| i ∈ 1 ... N }, conventional power unit i period t power output
PG,i,t, conventional power unit include non-Wind turbines and send interconnection equivalence unit outside, the wind power output that system can be dissolved in period t
PWIND,t；
Step 3: judge whether to need to translate load participation: ifThen consider machine a few days ago
The operation of group, spare, maintenance situation fully consider safety and the warp of system for the basic demand and fluctuation for meeting load
Ji property, preparatory rational management arrange the start and stop state d of t period 1-N generating setiWith power output PG,i,t, generation assets are rationally utilized,
Generate operation plan I a few days ago；IfIt then translates load and participates in scheduling；
Step 4: solve translatable load:
Objective function are as follows:
In formula: Lin,tFor the translation load that the t period is transferred to, P (Lin,t) it is the translation load L being transferred toin,tThe wind of corresponding consumption
Energy；
Constraint condition are as follows:
Translate period constraint:
In formula: sk,tFor the period may be transferred to earliest；dk,tFor the translation nargin of the translatable load of kth class, xk,t,t'For from t
Period goes to the translatable load cell number of k class of t' period；
Translational movement constraint:
In formula: xk,tFor the translatable load cell quantity of k class of t period；
Wind power constraint:
Step 5: judge whether that electricity is needed to turn the work of gas energy storage device: if
Then in the start and stop state d of schedule t period 1-N generating setiWith power output PG,i,tOn the basis of, consider translatable load type and
The translation load of all kinds of translatable load cells obtained with electrical characteristicsGenerate the tune a few days ago participated in containing translation load
Degree plan II；IfThen start electricity and turns gas energy storage device；
Step 6: starting electricity turns gas energy storage: enabling t period dump energyIt is remaining
Electric energy participates in electrolysis water reaction, and the hydrogen and carbon dioxide of generation carry out chemical reaction and generates methane；It is artificial synthesized by what is obtained
Natural gas CH4It is transported to natural gas system, the natural gas volume of conveying is determined by objective function:
BP neural network is using S type just using single hidden layer neural network, the neural transferring function of network middle layer
Transmission function is cut, output layer neuron transmission function is using S type to logarithmic transfer function.
Having the technical effect that acquired by by adopting the above technical scheme
1, the present invention can turn a part of wind-powered electricity generation of device of air consumption by electricity when wind power output is excessive, reduce to electric system
Impact.
2, the obtained artificial natural gas of the present invention directly can be stored and be transported in natural gas system, be reduced pair
The demand of energy-storage battery reduces energy storage cost.On the other hand, natural gas can supply a part of thermic load, reduce thermoelectricity
The pressure of factory.
Detailed description of the invention
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
Fig. 1 is flow chart of the invention；
Fig. 2 is load forecasting model block diagram；
Fig. 3 is that electricity turns gas flow chart.
Specific embodiment
Embodiment 1:
It is a kind of that the raising electric system of gas energy storage technology is turned to the digestion capability of wind-powered electricity generation using electricity based on control translation load
Method, comprising the following steps:
Step 1: carrying out wind power output prediction a few days ago and load prediction: wind-powered electricity generation is subjected to frequency domain decomposition, obtain diurnal periodicity, low
Frequency and three parts of high frequency.Diurnal periodicity partial rules clearly, regular sensitive advantage is fitted in conjunction with BP neural network
The system.Using single hidden layer neural network in the present invention, the neural transferring function of network middle layer is passed using the tangent of S type
Delivery function, output layer neuron transmission function is using S type to logarithmic transfer function.Pass through the low frequency portion obtained after frequency domain decomposition
Dividing is one section of very smooth curve, the prediction technique of the very high one-variable linear regression of precision is used in the present invention, by
The value for the two point prediction next points known, in addition, formula is as follows:
Y=3x2-x1 (1)
Wherein: x1It is known 1, x2It is the point for needing to predict for known 2, y；
The present invention decomposes high fdrequency component by Lifting Wavelet, and by the low frequency part of two layers of Lifting Wavelet obtained in it
It is input to training in the mathematical model of BP neural network and predicts.Finally the premeasuring of three components is superimposed, obtains wind-powered electricity generation
Prediction.Load is predicted by artificial neural network method: choosing the load of the past period as training sample, building
Suitable network structure is trained network with certain training algorithm, and after so that it is met required precision, this neural network is made
For load forecasting model；
Step 2: assessment wind electricity digestion capability I: all the period of time next day is denoted as t ∈ 1 ..., T, predicts that t period system is negative a few days ago
Lotus is PLOAD,t, predict that t period wind power output is a few days agoThus conventional power unit start and stop state, variable packet to be optimized are arranged
It includes: 1) conventional power unit open state D={ di| i ∈ 1 ... N } (single unit all the period of time start and stop state constant, di=1 i.e. unit i
Booting, di=0 i.e. unit i is shut down)；2) conventional power unit i (including non-Wind turbines and send interconnection equivalence unit outside) is in the period
The power output P of tG,i,t；3) the wind power output P that system can be dissolved in period tWIND,t.When plan arranges conventional power unit start and stop a few days ago, with
" ensuring that system is dissolved prediction gained wind-powered electricity generation by minimum abandonment mode " is principle, and optimization object function is
The constraint condition for needing to meet in optimization process includes:
1) the power balance constraint of period t, i.e.,
2) conventional power unit period t units limits, i.e.,
For non-Wind turbines,WithThe respectively minimum load and maximum output of unit；For contact
For line transimission power,WithRespectively send contracted period t minimum delivery electric power and maximum delivery electric power outside.
3) conventional power unit increases and decreases units limits, i.e.,
For non-Wind turbines,WithRespectively most increasing for unit contributes rate and maximum subtracts power output speed
Rate；For dominant eigenvalues,WithPower swing permissible value respectively under the constraint of interconnection appraisal standards
Bound.
4) wind power output constrains, i.e.,
5) system is constrained in the spinning reserve of period t, i.e.,
In formulaRespectively meet the positive spare capacity of minimum and minimal negative spare capacity of system safety requirements.
6) conventional power unit start and stop state constraint, i.e.,
In formula: can close conventional power unit is mainly the Coal-fired Thermal Power or combustion engine for allowing start and stop；Non- unit of closing keeps opening
Machine state, quantity of state diIt is fixed as 1；Unit booting or shutdown, quantity of state d can be closediDesirable 1 or 0.
Step 3: judge whether to need to translate load participation: ifIt then generates and adjusts a few days ago
Degree plan I；IfIt then translates load and participates in scheduling；
Step 4: solve translatable load: the target using translation load is that abandonment amount is minimum after making translation.
Objective function are as follows:
In formula: Lin,tFor the translation load that the t period is transferred to, P (Lin,t) it is the translation load L being transferred toin,tThe wind of corresponding consumption
Energy.
Constraint condition includes:
1) translation period constraint, i.e.,
In formula: sk,tFor the period may be transferred to earliest；dk,tFor the translation nargin of the translatable load of kth class, xk,t,t'For from t
Period goes to the translatable load cell number of k class of t' period.
2) translational movement constrains, i.e.,
In formula: xk,tFor the translatable load cell quantity of k class of t period.
3) wind power constrains, i.e.,
Step 5: judge whether that electricity is needed to turn the work of gas (P2G) energy storage device: if
Then generate operation plan II a few days ago；IfThen start electricity and turns gas (P2G) energy storage dress
It sets；
Step 6: starting electricity turns gas energy storage, the artificial synthesized natural gas CH that will be obtained4It is transported to natural gas system:
1) electrolysis water is reacted:
2) Sabatier catalysis reaction:
The objective function to be realized are as follows:
Step 7: measuring the real-time power output of wind-powered electricity generationIfThen turn to step 6；If
Then terminate to determine.
Claims (2)
1. a kind of turn the raising electric system of gas energy storage technology to the method for wind electricity digestion capability by electricity, it is characterised in that:
Step 1: t period system loading P is predicted in estimation a few days agoLOAD,tT period wind power output is predicted a few days agot∈1, … ,
T, 1-T are 24 periods of next day: it is made of step in detail below:
Step 1-1: the actual measurement wind power output data of past one month wind power plant are subjected to frequency domain decomposition, obtain frequency domain diurnal periodicity point
Amount, low frequency component and high fdrequency component；
Step 1-2: using the prediction model of BP neural network fitting frequency domain components diurnal periodicity；
Step 1-3: using the prediction model of one-variable linear regression method prediction low frequency component:
In formula:
--- t period wind-powered electricity generation low frequency component power output is predicted a few days ago；
--- known t1The practical power output of the low frequency component of period wind-powered electricity generation；
--- known t2The practical power output of the low frequency component of period wind-powered electricity generation；
Step 1-4: decomposing high fdrequency component by Lifting Wavelet, by the low frequency coefficient in obtained second layer Lifting Wavelet domain BP mind
Through network training high fdrequency component mathematical model；
Step 1-5: frequency domain components diurnal periodicity prediction model, low frequency component prediction model and high fdrequency component mathematical model are used respectively
Prediction a few days ago wind power output diurnal periodicity frequency domain components predicted value, low frequency component predicted value and high fdrequency component predicted value；
Step 1-6: by wind power output a few days ago diurnal periodicity frequency domain components predicted value, low frequency component predicted value and high fdrequency component predict
Value is cumulative, obtains the predicted value of wind power output a few days ago；
Step 1-7: load is predicted by artificial neural network method: constructs the network structure of load forecasting model, chosen
Past two weeks history daily loads are trained the coefficient of load forecasting model as training sample, meet it preset
Required precision；
Step 2: by " ensure system to prediction gained wind-powered electricity generation by minimum abandonment in a manner of dissolve " for principle, optimize wind electricity digestion energy
Power:
Objective function is
In formula:
--- t period wind power output is predicted a few days ago；
PWIND,t--- the wind power output that system can be dissolved in the t period；
PLOAD,t--- t period system loading is predicted a few days ago；
di--- conventional power unit open state D={ di| i ∈ 1 ... N }, single unit all the period of time start and stop state constant, di=1 is
Unit booting, di=0 i.e. unit i is shut down；
PG,i,t--- power output of the conventional power unit i in period t；Conventional power unit includes non-Wind turbines and sends the check-ins such as interconnection outside
Group；
Constraint condition are as follows:
The power balance of period t constrains
Units limits of the conventional power unit in period t:
For non-Wind turbines,WithThe respectively minimum load and maximum output of unit；Function is transmitted for interconnection
Rate,WithRespectively send contracted period t minimum delivery electric power and maximum delivery electric power outside；
Conventional power unit increases and decreases units limits:
For non-Wind turbines,WithRespectively most increasing for unit contributes rate and maximum subtracts power output rate；
For dominant eigenvalues,WithRespectively the lower power swing permissible value of interconnection appraisal standards constraint is upper and lower
Limit；
Wind power output constraint:
Spinning reserve of the system in period t constrains:
In formulaRespectively meet the positive spare capacity of minimum and minimal negative spare capacity of system safety requirements；
Conventional power unit start and stop state constraint:
In formula: the non-quantity of state d for closing unitiIt is 1；When can close unit in booting or off-mode, quantity of state diPhase
1 or 0 should be taken；
Variable to be optimized is conventional power unit open state collection D={ di| i ∈ 1 ... N }, conventional power unit i period t power output
PG,i,t, conventional power unit include non-Wind turbines and send interconnection equivalence unit outside, the wind power output that system can be dissolved in period t
PWIND,t；
Step 3: judge whether to need to translate load participation: ifThen consider unit a few days ago
Operation, spare, maintenance situation, preparatory rational management arrange the start and stop state d of t period 1- N platform generating setiAnd power output
PG,i,t, generate operation plan I a few days ago；IfIt then translates load and participates in scheduling；
Step 4: solve translatable load:
Objective function are as follows:
In formula:To translate load；
Constraint condition are as follows:
Translate period constraint:
In formula: sk,tFor the period may be transferred to earliest；dk,tFor the translation nargin of the translatable load of kth class, xk,t,t'For from the t period
Go to the translatable load cell number of k class of t' period；
Translational movement constraint:
In formula: xk,tFor the translatable load cell quantity of k class of t period；
Wind power constraint:
Step 5: judge whether that electricity is needed to turn the work of gas energy storage device: ifThen adjusting
Degree arranges the start and stop state d of the 1-the N of t period platform generating setiWith power output PG,i,tOn the basis of, consider translatable load type
And the translation load of all kinds of translatable load cells obtained with electrical characteristicsIt generates and is participated in a few days ago containing translation load
Operation plan II；IfThen start electricity and turns gas energy storage device；
Step 6: starting electricity turns gas energy storage: enabling t period dump energyDump energy
Electrolysis water reaction is participated in, the hydrogen and carbon dioxide of generation carry out chemical reaction and generates methane, artificial synthesized natural by what is obtained
Gas CH4It is transported to natural gas system, the natural gas volume of conveying is determined by objective function:
Step 7: measuring the real-time power output of wind-powered electricity generationIfThen turn to step 6；If
Then terminate to determine.
It gas energy storage technology is turned by electricity improves electric system 2. according to claim 1 to the method for wind electricity digestion capability,
It is characterized by: BP neural network is using single hidden layer neural network, the neural transferring function of network middle layer is using S type
Tangent transfer function, output layer neuron transmission function is using S type to logarithmic transfer function.
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| CN107404125B (en) * | 2017-07-26 | 2020-06-30 | 南方电网科学研究院有限责任公司 | Multi-energy complementary power distribution system and energy management method and device thereof |
| CN107748934B (en) * | 2017-10-26 | 2023-06-23 | 国网新疆电力公司经济技术研究院 | Distribution network electric heating load prediction method based on improved BP neural network algorithm |
| CN108258721A (en) * | 2017-12-30 | 2018-07-06 | 国家电网公司华北分部 | Wind electricity digestion capability acquisition methods and system under a kind of unit safety constraint |
| CN108512257A (en) * | 2018-04-11 | 2018-09-07 | 湘潭大学 | A kind of industrial load fluctuation tolerance containing wind electric system determines method |
| CN108808713B (en) * | 2018-05-04 | 2020-05-22 | 国网内蒙古东部电力有限公司电力科学研究院 | Industrial heat load control system and method for improving new energy power generation absorption capacity |
| CN109412181B (en) * | 2018-11-06 | 2020-06-02 | 湖南大学 | Multi-energy frame based on conversion of new energy into ammonia gas, consumption method, medium and equipment |
| CN111030110B (en) * | 2019-12-28 | 2021-05-18 | 福州大学 | Robust cooperative scheduling method for electric power-natural gas coupling system considering electric power conversion gas consumption wind power |
| CN111160653B (en) * | 2019-12-31 | 2023-06-27 | 国网内蒙古东部电力有限公司经济技术研究院 | Distributed energy storage system wind power consumption capability monitoring method based on cloud computing |
| CN113659632B (en) * | 2021-08-10 | 2024-01-23 | 中国华能集团清洁能源技术研究院有限公司 | Electrolytic hydrogen production system capable of realizing large-scale fluctuation energy consumption and operation method |
| CN114253238A (en) * | 2021-12-14 | 2022-03-29 | 浙江中控技术股份有限公司 | License plate switching method and device, electronic equipment and storage medium |
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