CN106877409A - Gas energy storage technology is turned by electricity and improves method of the power system to wind electricity digestion capability - Google Patents
Gas energy storage technology is turned by electricity and improves method of the power system to wind electricity digestion capability Download PDFInfo
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Abstract
Method of the gas energy storage technology raising power system to wind electricity digestion capability is turned by electricity the invention discloses a kind of, including prediction a few days ago t periods system loading and a few days ago t periods wind power output, with " ensuring that system is abandoned wind mode and dissolved to prediction gained wind-powered electricity generation by minimum " for principle, the scheduling scheme I of setting optimization wind electricity digestion capability, judge whether to need translation load to participate in, solve translatable load, judging whether to need electricity to turn gas energy storage device to work and start electric energy storage step.The present invention can turn device of air and dissolve a part of wind-powered electricity generation when wind power output is excessive by electricity, reduce the impact to power system, the artificial natural gas that it is obtained directly can be stored and transported in natural gas system, reduce the demand to energy-storage battery, 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
Gas is turned by electricity the present invention relates to a kind of method for improving power system to wind electricity digestion capability, more particularly to one kind
Energy storage technology improves power system to the method for wind electricity digestion capability, belongs to technical field of new energies.
Background technology
It is one of principal mode of development new energy that large-scale wind power is grid-connected, is the important set of China's new energy development strategy
Into part.With the rapid growth of China's installed capacity of wind-driven power, the grid-connected problem of dissolving of large-scale wind power is increasingly protruded.Wind-powered electricity generation sheet
The randomness and fluctuation of body so that the power output of wind power plant has very strong randomness, and foot is not reaching in forecasting wind speed
Under conditions of enough precision, the wind changed power and predicated error of hour level mean to cause power system very big impact.This
Outward, THE WIND ENERGY RESOURCES IN CHINA galore offset from load center farther out, large-scale wind generate electricity cannot on-site elimination, it is necessary to pass through defeated
Power network remote conveying is sent to load center.A large amount of wind power long-distance sand transports often cause system voltage significantly to change, office
The voltage stability of portion's power network is affected, stability margin reduction.
In order to improve digestion capability of the power system to wind-powered electricity generation, notification number is the Chinese invention patent of CN105656025A,
Disclose a kind of method for improving wind electricity digestion capability.The method is to predict the wind power output of second day and load data
On the basis of, determine whether to abandon wind appearance, so as to be controlled to translation load, operation plan a few days ago is generated, to a certain degree
On can improve the usage amount of wind-powered electricity generation.But, because the fluctuation and randomness of wind energy are strong, precisely prediction is difficult to realize, work as wind
When fast excessive suddenly, just have and substantial amounts of abandon wind or occur cutting machine.Liu Xin side et al. in electric power system protection and control,
2012,40(6):Disclose a kind of logical in 35-39. " using the theoretical research for rationally abandoning wind raising large-scale wind power digestion capability "
The method that wind abandons the part wind-powered electricity generation generating capacity future extensive wind electricity digestion capability high is abandoned after rationally, is distributed using weibull
Simultaneously foundation is analyzed in conduct to fitting equivalent load curve, and quantization is abandoned wind power to the improvement coefficient of peak regulation and abandons wind to wind-powered electricity generation generating appearance
The influence of amount.The method can to a certain extent improve large-scale wind power digestion capability, but be to sacrifice a part of wind energy
It is cost.
Energy-storage system can effectively suppress wind power fluctuation, smooth output voltage, improve the quality of power supply, be to ensure wind-force
Electricity generation grid-connecting runs, and promotes the key technology of Wind Power Utilization.King rushes et al. in regenerative resource again, and 2014,32 (7)：954-
Disclose a kind of at the beginning of Wind Power Generation in 960. " the power source planning research of wind electricity digestion capability is improved based on energy-accumulating power station ", from
Planning aspect optimizes regional power system power supply architecture using energy-accumulating power station, so as to improve side of the power network to the digestion capability of wind-powered electricity generation
Method.It is maximum with national economy input most with wind electricity digestion capability after considering environmental benefit, social benefit, the market competitiveness
Small is object 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. " improving the energy storage Optimal Configuration Method of wind electricity digestion capability " discloses one kind and considers
The energy storage Stochastic Programming Model of wind storage system, conventional power unit and power network correlation, it is proposed that to abandon wind ratio as constraint, investment
The energy storage power and capacity ratio optimization method of cost minimization.
The high cost of current energy-storage system constrains its application, and with cost minimization as object function, it is possible to make
Into abandoning the too many problem of air quantity.And stored energy capacitance is limited, during wind-powered electricity generation wide fluctuations, energy storage does not have sufficiently large capacity empty
Between, in addition, wind power output less season, stored energy capacitance configuration may be superfluous, and energy-storage system is not fully utilized.
The content of the invention
The technical problem to be solved in the present invention is to provide one kind and turns the raising power system of gas energy storage technology to wind-powered electricity generation by electricity
The method of digestion capability.
In order to solve the above technical problems, the technical solution adopted by the present invention is：
It is a kind of that the raising power system of gas energy storage technology is turned to the method for wind electricity digestion capability by electricity, comprise the following steps：
Step 1：Predict t period system loadings P a few days agoLOAD, tT period wind power outputs a few days agoT ∈ 1 ..., T, 1-
T is 24 periods of next day：It is made up of step in detail below：
Step 1-1：The actual measurement wind power output data of past one month wind power plant are carried out into frequency domain decomposition, diurnal periodicity is obtained frequently
Domain component, low frequency component and high fdrequency component；
Step 1-2：Using BP neural network fitting frequency domain components forecast model diurnal periodicity；
Step 1-3：Low frequency component forecast model is predicted using one-variable linear regression method：
In formula：
--- t period wind power outputs are predicted a few days ago；
--- known t1Period wind-powered electricity generation is actually exerted oneself；
--- known t2Period wind-powered electricity generation is actually exerted oneself；
Step 1-4：High fdrequency component is decomposed by Lifting Wavelet, the low frequency coefficient in the second layer Lifting Wavelet domain that will be obtained is used
BP neural network trains high fdrequency component Mathematical Modeling；
Step 1-5：Frequency domain components forecast model diurnal periodicity, low frequency component forecast 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 adds up, and obtains the predicted value of wind power output a few days ago；
Step 1-7：Load is predicted by artificial neural network method：The network structure of load forecasting model is built,
The history daily load of two weeks is chosen as training sample, the coefficient to load forecasting model is trained, and meets it default
Required precision；
Step 2：Wind electricity digestion, for principle, is optimized with " ensuring that system is abandoned wind mode and dissolved to prediction gained wind-powered electricity generation by minimum "
Ability：
Object function is：
In formula：
--- t period wind power outputs are predicted a few days ago；
PWIND,t--- the wind power output that system can dissolve in the t periods；
PLOAD,t--- t period system loadings are 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
That is unit start, di=0 i.e. unit i shuts down；
PG,i,t--- conventional power unit i (including non-Wind turbines with send outside interconnection equivalence unit) exerting oneself in period t.
Object function is accomplished that, by planning to arrange conventional power unit start and stop a few days ago, it is ensured that system is to the wind obtained by prediction
The electricity amount of dissolving is maximum, that is, abandon air quantity minimum.
Constraints is：
The power balance constraint of period t：
Units limits of the conventional power unit in period t：
For non-Wind turbines,WithThe respectively minimum load and EIAJ of unit；Transmitted for interconnection
Power,WithRespectively send contracted period t minimum deliveries electric power and maximum delivery electric power outside；
Conventional power unit increases and decreases units limits：
For non-Wind turbines,WithExert oneself speed and the maximum of most increasing of respectively unit subtracts speed of exerting oneself
Rate；For dominant eigenvalues,WithPower swing permissible value respectively under the constraint of interconnection appraisal standards
Bound；
Wind power output is constrained：
System is constrained in the spinning reserve of period t：
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 start or off-mode, its state
Amount 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 exerts oneself period t's
PG,i,t, conventional power unit includes non-Wind turbines and sends the wind power output that interconnection equivalence unit, system can dissolve in period t outside
PWIND,t；
Step 3：Judge whether to need translation load to participate in：IfThen consider machine a few days ago
The operation of group, standby, maintenance situation, to meet basic demand and the fluctuation of load, take into full account the security and warp of system
Ji property, advance rational management arranges the start and stop state d of t period 1-N generating setsiWith the P that exerts oneselfG,i,t, rationally using generation assets,
Generate operation plan I a few days ago；IfThen translation load participates in scheduling；
Step 4：Solve translatable load：
Object function is：
In formula：Lin,tIt is the translation load that the t periods are transferred to, P (Lin,t) it is the translation load L being transferred toin,tThe wind of correspondence consumption
Energy；
Constraints is：
The constraint of translation period：
In formula：sk,tFor the period may be transferred to earliest；dk,tIt is the translation nargin of the translatable load of kth class, xk,t,t'It is from t
Period goes to the translatable load cell number of k classes of t' periods；
Translational movement is constrained：
In formula：xk,tIt is the translatable load cell quantity of the k for being powered in the t periods originally；
Wind power is constrained：
Step 5：Judge whether that needing electricity to turn gas energy storage device works：If
Then in the start and stop state d of schedule t period 1-N generating setsiWith the P that exerts oneselfG,i,tOn the basis of, it is considered to translatable load species and
The translation load obtained with electrical characteristics of all kinds of translatable load cellsThe tune a few days ago that generation is participated in containing translation load
Degree plan II；IfThen start electricity and turn gas energy storage device；
Step 6：Start electricity and turn gas energy storage.Electric energy generates hydrogen and oxygen by electrolysis water, recycles hydrogen and titanium dioxide
Carbon carries out chemical reaction and produces methane.The artificial synthesized natural gas CH that will be obtained4Natural gas system is transported to, conveying capacity is by target
Function determines：
BP neural network is using S types just using single hidden layer neutral net, the neural transferring function in network intermediate layer
Transmission function is cut, output layer neural transferring function is using S types to logarithmic transfer function.
Using having technical effect that acquired by above-mentioned technical proposal：
1st, the present invention can turn device of air and dissolves a part of wind-powered electricity generation when wind power output is excessive by electricity, reduce to power system
Impact.
2nd, the artificial natural gas that the present invention is obtained directly can be stored and transported in natural gas system, and it is right to reduce
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.
Brief description of the drawings
The present invention is further detailed explanation with reference to the accompanying drawings and detailed description.
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 digestion capability of the gas energy storage technology raising power system to wind-powered electricity generation is turned using electricity based on control translation load
Method, comprise the following steps：
Step 1：Carry out wind power output prediction a few days ago and load prediction：Wind-powered electricity generation is carried out into frequency domain decomposition, diurnal periodicity, low is obtained
Frequency and three parts of high frequency.Diurnal periodicity partial rules clearly, with reference to the BP neural network advantage fitting sensitive to rule
The system.Using single hidden layer neutral net in the present invention, the neural transferring function in network intermediate layer is passed using the tangent of S types
Delivery function, output layer neural transferring function is using S types to logarithmic transfer function.By the low frequency part obtained after frequency domain decomposition
It is one section of curve of unusual light, with the Forecasting Methodology of precision one-variable linear regression very high in the present invention, by known
Two point prediction subsequent points value, separately, formula is as follows：
Y=3x2-x1 (1)
Wherein：x1It is known 1, x2It is the point that known 2, y is needs prediction；
The present invention decomposes high fdrequency component by Lifting Wavelet, and the two layers of low frequency part of Lifting Wavelet that will wherein obtain
It is input to training and prediction in the Mathematical Modeling of BP neural network.Finally by three premeasuring superpositions of component, wind-powered electricity generation is obtained
Prediction.Load is predicted by artificial neural network method：The load of the past period is chosen as training sample, is built
Suitable network structure, is trained with certain training algorithm to network, and after it is met required precision, this neutral net is made
It is load forecasting model；
Step 2：Assessment wind electricity digestion capability I：All the period of time next day is designated as t ∈ 1 ..., T, and t period system loadings are predicted a few days ago
It is PLOAD,t, predict that t period wind power outputs are a few days agoThus conventional power unit start and stop state, variable bag to be optimized are arranged
Include：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 is opened
Machine, di=0 i.e. unit i shuts down)；2) conventional power unit i (including non-Wind turbines with send outside interconnection equivalence unit) period t's
Exert oneself PG,i,t；3) the wind power output P that system can dissolve in period tWIND,t.When plan arranges conventional power unit start and stop a few days ago, with " really
Insurance system is abandoned wind mode and is dissolved to prediction gained wind-powered electricity generation by minimum " it is principle, optimization object function is
The constraints for meeting is needed in optimization process to be included：
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 EIAJ of unit；For interconnection
For transimission power,WithRespectively send contracted period t minimum deliveries electric power and maximum delivery electric power outside.
3) conventional power unit increase and decrease units limits, i.e.,
For non-Wind turbines,WithExert oneself speed and the maximum of most increasing of respectively unit subtracts speed of exerting oneself
Rate；For dominant eigenvalues,WithPower swing permissible value respectively under the constraint of interconnection appraisal standards
Bound.
4) wind power output constraint, 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：Conventional power unit can be closed and be 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 start can be closed or shut down, quantity of state diDesirable 1 or 0.
Step 3：Judge whether to need translation load to participate in：IfThen generate and adjust a few days ago
Degree plan I；IfThen translation load participates in scheduling；
Step 4：Solve translatable load：The use of the target of translation load is to abandon air quantity minimum after making translation.
Object function is：
In formula：Lin,tIt is the translation load that the t periods are transferred to, P (Lin,t) it is the translation load L being transferred toin,tThe wind of correspondence consumption
Energy.
Constraints includes：
1) translation period constraint, i.e.,
In formula：sk,tFor the period may be transferred to earliest；dk,tIt is the translation nargin of the translatable load of kth class, xk,t,t'It is from t
Period goes to the translatable load cell number of k classes of t' periods.
2) translational movement constraint, i.e.,
In formula：xk,tIt is the translatable load cell quantity of the k for being powered in the t periods originally.
3) wind power constraint, i.e.,
Step 5：Judge whether that needing electricity to turn gas (P2G) energy storage device works：If
Then generate operation plan II a few days ago；IfThen start electricity and turn gas (P2G) energy storage dress
Put；
Step 6：Start electricity and turn gas energy storage, the artificial synthesized natural gas CH that will be obtained4It is transported to natural gas system：
1) electrolysis water reaction：
2) Sabatier catalytic reactions：
The object function to be realized is：
Step 7:The real-time of measurement wind-powered electricity generation is exerted oneselfIfThen turn to step 6；IfThen terminate to judge.
Claims (2)
- It is 1. a kind of that method of the gas energy storage technology raising power system to wind electricity digestion capability is turned by electricity, it is characterised in that：Step 1：Estimate prediction t period system loadings P a few days agoLOAD,tT period wind power outputs are predicted a few days agot∈ 1 ..., T, 1-T are 24 periods of next day：It is made up of step in detail below：Step 1-1：The actual measurement wind power output data of past one month wind power plant are carried out into frequency domain decomposition, frequency domain point diurnal periodicity is obtained Amount, low frequency component and high fdrequency component；Step 1-2：Using the forecast model of BP neural network fitting frequency domain components diurnal periodicity；Step 1-3：The forecast model of low frequency component is predicted using one-variable linear regression method：In formula：--- predict that t period wind-powered electricity generation low frequency components are exerted oneself a few days ago；--- known t1The low frequency component of period wind-powered electricity generation is actual to exert oneself；--- known t2The low frequency component of period wind-powered electricity generation is actual to exert oneself；Step 1-4：High fdrequency component is decomposed by Lifting Wavelet, the low frequency coefficient in the second layer Lifting Wavelet domain that will be obtained is refreshing with BP Through network training high fdrequency component Mathematical Modeling；Step 1-5：Frequency domain components forecast model diurnal periodicity, low frequency component forecast model and high fdrequency component Mathematical Modeling 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 prediction 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：The network structure of load forecasting model is built, is chosen The history daily load in past two weeks is trained as training sample, the coefficient to load forecasting model, meets it default Required precision；Step 2：Wind electricity digestion energy, for principle, is optimized with " ensuring that system is abandoned wind mode and dissolved to prediction gained wind-powered electricity generation by minimum " Power：Object function isIn formula：--- t period wind power outputs are predicted a few days ago；PWIND,t--- the wind power output that system can dissolve in the t periods；PLOAD,t--- t period system loadings are 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. machine Group start, di=0 i.e. unit i shuts down；PG,i,t--- conventional power unit i exerts oneself period t's；Conventional power unit including non-Wind turbines with send the check-ins such as interconnection outside Group；Constraints is：The power balance constraint of period tUnits limits of the conventional power unit in period t：For non-Wind turbines,WithThe respectively minimum load and EIAJ of unit；Work(is transmitted for interconnection Rate,WithRespectively send contracted period t minimum deliveries electric power and maximum delivery electric power outside；Conventional power unit increases and decreases units limits：For non-Wind turbines,WithExert oneself speed and the maximum of most increasing of respectively unit subtracts speed of exerting oneself； For dominant eigenvalues,WithRespectively interconnection appraisal standards constraint under power swing permissible value it is upper and lower Limit；Wind power output is constrained：System is constrained in the spinning reserve of period t：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 start or off-mode, its 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 the P that exerts oneselfG,i,t, Conventional power unit includes non-Wind turbines with the wind power output P for sending interconnection equivalence unit outside, system can dissolve in period tWIND,t；Step 3：Judge whether to need translation load to participate in：IfThen consider unit a few days ago Operation, standby, maintenance situation, advance rational management arrange the start and stop state d of t period 1- N platform generating setsiWith exert oneself PG,i,t, generate operation plan I a few days ago；IfThen translation load participates in scheduling；Step 4：Solve translatable load：Object function is：In formula：It is translation load；Constraints is：The constraint of translation period：In formula：sk,tFor the period may be transferred to earliest；dk,tIt is the translation nargin of the translatable load of kth class, xk,t,t'It is from the t periods Go to the translatable load cell number of k classes of t' periods；Translational movement is constrained：In formula：xk,tIt is the translatable load cell quantity of the k for being powered in the t periods originally；Wind power is constrained：Step 5：Judge whether that needing electricity to turn gas energy storage device works：IfThen adjusting Degree arranges the start and stop state d of the 1-the N of t periods platform generating setiWith the P that exerts oneselfG,i,tOn the basis of, it is considered to translatable load species And the translation load obtained with electrical characteristics of all kinds of translatable load cellsGeneration is participated in a few days ago containing translation load Operation plan II；IfThen start electricity and turn gas energy storage device；Step 6：Start electricity and turn gas energy storage.Electric energy generates hydrogen and oxygen by electrolysis water, recycles hydrogen to enter with carbon dioxide Row chemical reaction produces methane.The artificial synthesized natural gas CH that will be obtained4Natural gas system is transported to, conveying capacity is by object function It is determined that：。
- It is 2. according to claim 1 that method of the gas energy storage technology raising power system to wind electricity digestion capability is turned by electricity, It is characterized in that：, using single hidden layer neutral net, the neural transferring function in network intermediate layer is using S types for BP neural network Tangent transfer function, output layer neural transferring function is using S types to logarithmic transfer function.
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