CN104967149B - A kind of micro-capacitance sensor wind-light storage model predictive control method - Google Patents
A kind of micro-capacitance sensor wind-light storage model predictive control method Download PDFInfo
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
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Abstract
The invention discloses a kind of micro-capacitance sensor wind-light storage model predictive control method, comprise the following steps：Forecast model is set up, the EIAJ of Wind turbines and photovoltaic generation in micro-capacitance sensor in the following setting period is predicted by forecast model；Using the wind-powered electricity generation and photovoltaic EIAJ that predict as constraints, carry out on-line optimization of being exerted oneself to Wind turbines, photovoltaic generation and energy-storage battery three in micro-capacitance sensor, the reference for providing three is exerted oneself；According to Wind turbines and the real-time, tunable capacity of photovoltaic generation, to Wind turbines, photovoltaic generation and energy-storage battery three with reference to carry out feedback adjustment of exerting oneself.Compared with traditional grid control method, the control method reduces the requirement to the precision of forecasting model of uncertain course, compensate for that the insoluble wind-powered electricity generation of traditional control method and photovoltaic precision of forecasting model are low, uncertain strong defect of exerting oneself, effectively improve the operation characteristic of micro-capacitance sensor.
Description
Technical field
The present invention relates to a kind of micro-capacitance sensor wind-light storage model predictive control method.
Background technology
Distributed power generation has the advantages that resource and environment-friendly, power supply are flexible, in centralization generating and the base of bulk power grid
Develop distributed power generation on plinth, have become the inexorable trend of domestic and international intelligent grid development.According to national energy development plan,
9% is up to the year two thousand twenty China distributed power generation installed capacity accounting, is the important component in supply of electric power system.For
The problem of distributed power generation is grid-connected to be brought is solved, the concept of micro-capacitance sensor is proposed.Comprehensive achievement in research both domestic and external, micro-capacitance sensor
Refer to based on generation technology in a distributed manner, based on regenerative resource, using energy storage and control device, realize network internal electricity
The miniature supply network of power electric quantity balancing.
The uncertainty that wind-powered electricity generation and photovoltaic are exerted oneself is big, it is difficult to predict, its precision of prediction is also very low, and prediction in advance when
Between it is longer, its predicated error is bigger.The access of wind-powered electricity generation and photovoltaic makes the uncertain increase that micro-capacitance sensor is run, existing micro-capacitance sensor
Control method requires higher to the precision of forecasting model of uncertain course, therefore uncertain in the urgent need to seeking more preferably to tackle
The control method of property.
Model Predictive Control (model predictive control, MPC) is the effective way for solving this problem.Mould
Type PREDICTIVE CONTROL is widely used always in industrial stokehold, and its adaptability and robustness to model is stronger, fits very much
Close the problem of reply system model is uncertain big.Model Predictive Control is substantially finite time-domain closed loop of the class based on model
Optimal control algorithm, in each sampling period, original state of the controller using the system mode at current time as control is based on
Forecast model predicts the outcome to to-be, has the optimal control problem of limit to obtain by rolling solution one online
Obtain controlling behavior currently so that the difference of future output and reference locus is minimum.
The content of the invention
The present invention is in order to solve the above problems, it is proposed that a kind of micro-capacitance sensor wind-light storage model predictive control method, this method
The thought of Model Predictive Control is used for reference, the Controlling model of prediction-on-line optimization-feedback is established, is predicted by forecast model
In the following certain period in micro-capacitance sensor Wind turbines and photovoltaic generation EIAJ；The wind-powered electricity generation and light that forecast model is predicted
EIAJ is lied prostrate as constraints, progress of being exerted oneself to Wind turbines, photovoltaic generation and energy-storage battery three in micro-capacitance sensor exists
Line optimizes, and the reference for providing three in following certain period is exerted oneself；According to Wind turbines and the real-time, tunable capacity of photovoltaic generation,
In control moment to wind-light storage three with reference to carry out feedback adjustment of exerting oneself.Embodiment analysis shows, this method can be answered preferably
The uncertainty exerted oneself to wind-powered electricity generation and photovoltaic, is effectively improved the operation characteristic of micro-capacitance sensor.
To achieve these goals, the present invention is adopted the following technical scheme that：
A kind of micro-capacitance sensor wind-light storage model predictive control method, comprises the following steps：
(1) forecast model is set up, passes through Wind turbines and photovoltaic in micro-capacitance sensor in the forecast model prediction following setting period
The EIAJ of generating；
(2) using the wind-powered electricity generation and photovoltaic EIAJ that predict as constraints, Wind turbines, photovoltaic in micro-capacitance sensor are sent out
Electricity and energy-storage battery three are exerted oneself carry out on-line optimization, and the reference for providing three is exerted oneself；
(3) according to Wind turbines and the real-time, tunable capacity of photovoltaic generation, to Wind turbines, photovoltaic generation and energy storage electricity
Pond three is with reference to carry out feedback adjustment of exerting oneself.
In the step (1), forecast model realizes the work(of Wind turbines and photovoltaic generation by neural network technology
Rate is predicted, according to the historical information of process and the following output valve of following input prediction process.
The method for building up of forecast model is neural network technology in the present invention, and this method process is ripe in the industry
Know.
The object function of on-line optimization model in the step (2)：Object function f1Exchanged for micro-capacitance sensor with power distribution network
Power deviation is minimum, to ensure that micro-capacitance sensor turns into the stable power supply or load of power distribution network, reduces the control difficulty of power distribution network,
Under micro-capacitance sensor off-network state, it is 0, object function f to set micro-capacitance sensor to exchange power with power distribution network2The last period lotus at the beginning of energy-storage battery
The difference of electricity condition is minimum, to ensure that energy-storage battery has higher electricity；
minf2=SOC (1)-SOC (N)
Wherein, N is when hop count total in optimization time domain；Pl(i) it is the load power in period i；Ptie(i) in period i
Micro-capacitance sensor exchanges power with power distribution network, and on the occasion of representing micro-capacitance sensor to power distribution network injecting power, negative value represents power distribution network to micro- electricity
Net injecting power；Pw(i) exerted oneself for plan of the Wind turbines in period i；PPV(i) it is photovoltaic generation being planned out in period i
Power；Pb(i) exerted oneself for plan of the energy-storage battery in period i, the value is on the occasion of the value is negative value during electric discharge during charging；SOC
(1), SOC (N) is the 1st period, the energy-storage battery state-of-charge of N periods.
The constraints of on-line optimization model in the step (2)：Exerted oneself including Wind turbines, photovoltaic generation exert oneself with
And energy-storage battery discharge and recharge count constraint：
0 ＜ Pw(i) ＜ Pw_pre(i)
0 ＜ PPV(i) ＜ PPV_pre(i)
Nch_dis＜ Nbattery
Wherein, Pw_pre(i) exerted oneself predicted value for the Wind turbines in period i；Ppv_pre(i) it is the photovoltaic generation in period i
Exert oneself predicted value；Nch_disFor energy-storage battery discharge and recharge number of times；NbatteryFor the maximum allowable discharge and recharge number of times of energy-storage battery.
The derivation algorithm specific method of on-line optimization model in the step (2) is：The Optimized model includes 2 targets
Function, belongs to multi-objective optimization question, and multi-objective optimization algorithm has 3 Performance Evaluating Indexes：1. the solution tried to achieve will connect as far as possible
Nearly Pareto optimal solutions；2. the distributivity and diversity of solution colony are kept as far as possible；3. to prevent what is obtained in solution procedure
Pareto optimal solutions are lost, using the Algorithm for Solving on-line optimization models of NSGA- II.
The specific method of real-time, tunable capacity in the step (3) is：Wind turbines real-time, tunable capacity △ PwFor：
ΔPw=Pw_est-Pw
Wherein, PwExerted oneself for Wind turbines reference；Pw_estExerted oneself in real time estimate for Wind turbines, the estimate can pass through
Real-time wind speed and fan operation state are quickly calculated；
Photovoltaic generation real-time, tunable capacity △ PpvFor：
ΔPpv=Ppv_est-Ppv
Wherein, PpvExerted oneself for photovoltaic generation reference；Ppv_estExerted oneself in real time estimate for photovoltaic generation, the estimate passes through
Real-time lighting intensity and temperature computation come out.
Feedback adjustment in the step (3)：Variable capacity, feedback adjustment ring are whether there is according to Wind turbines and photovoltaic generation
Section specifically includes following 4 kinds of situations：
(a) Wind turbines and photovoltaic generation are respectively provided with variable capacity, you can capacitance-adjustable is that just, will now refer to and exert oneself directly
It is handed down to Wind turbines, photovoltaic generation and energy-storage battery；
(b) Wind turbines and photovoltaic generation are without variable capacity, you can capacitance-adjustable is negative, will now be referred under exerting oneself directly
Issue Wind turbines and photovoltaic generation, energy-storage battery compensation power shortage；
(c) Wind turbines have variable capacity, and photovoltaic generation is without variable capacity, and photovoltaic generation has superfluous plan, now
Photovoltaic generation surplus plan is transferred to Wind turbines；
(d) photovoltaic generation has variable capacity, and Wind turbines are without variable capacity, and Wind turbines have superfluous plan, now
Wind turbines surplus plan is transferred to photovoltaic generation.
In the step (c), with reference to exerting oneself, adjustment is as follows：
Pw_sch=Pw+min(ΔPw,-ΔPpv)
Ppv_sch=Ppv-min(ΔPw,-ΔPpv)
Wherein, Pw_schExerted oneself for Wind turbines last minute planning；Ppv_schExerted oneself for photovoltaic generation last minute planning.
In the step (d), with reference to exerting oneself, adjustment is as follows：
Ppv_sch=Ppv+min(ΔPpv,-ΔPw)
Pw_sch=Pw_est-min(ΔPpv,-ΔPw)。
Beneficial effects of the present invention are：
(1) thought of Model Predictive Control is used for reference, the invention provides a kind of micro-capacitance sensor wind-light storage Model Predictive Control side
Method, establishes the Controlling model of prediction-on-line optimization-feedback；
(2) control method employs the on-line optimization strategy set up on the basis of reality output feedback so that controlled
Journey can be in time to predicated error influence make amendment；
(3) compared with traditional grid control method, the control method reduces the forecast model to uncertain course
The requirement of precision, compensate for that the insoluble wind-powered electricity generation of traditional control method and photovoltaic precision of forecasting model are low, it is uncertain to exert oneself
The strong defect of property, effectively improves the operation characteristic of micro-capacitance sensor.
Brief description of the drawings
Fig. 1 is the micro-capacitance sensor wind-light storage model predictive control method schematic flow sheet that the present invention is provided；
Fig. 2 is wind-powered electricity generation prediction EIAJ and actual EIAJ curve synoptic diagram in the embodiment of the present invention；
Fig. 3 is photovoltaic prediction EIAJ and actual EIAJ curve synoptic diagram in the embodiment of the present invention；
Fig. 4 is wind-light storage optimization reference power curve schematic diagram in the embodiment of the present invention；
Fig. 5 is that scene refers to power curve schematic diagram after feedback adjustment in the embodiment of the present invention；
Fig. 6 is the real-time power curve schematic diagram of wind-light storage in the embodiment of the present invention；
Fig. 7 is energy-storage battery SOC change curve schematic diagrames in the embodiment of the present invention.
Embodiment：
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
A kind of micro-capacitance sensor wind-light storage model predictive control method, comprises the following steps：
Step (1)：By Wind turbines in micro-capacitance sensor in forecast model prediction following certain period and photovoltaic generation most
Exert oneself greatly；
Step (2)：The wind-powered electricity generation and photovoltaic EIAJ that step (1) is predicted are as constraints, to micro-capacitance sensor apoplexy
Group of motors, photovoltaic generation and energy-storage battery three exert oneself carry out on-line optimization, provide the reference of three in following certain period
Exert oneself；
Step (3)：According to Wind turbines and the real-time, tunable capacity of photovoltaic generation, in wind of the control moment to step (2)
Light storage three is with reference to carry out feedback adjustment of exerting oneself.
Forecast model in the step (1)：The function of forecast model is the historical information and following input according to process
The following output valve of prediction process, priori is provided for the optimization of Model Predictive Control.Forecast model only focuses on the work(of model
Can, the form without focusing on model, as long as the model with the following dynamic function of forecasting system, no matter which type of performance it has
Form, can be used as forecast model.In the present invention, forecast model realizes Wind turbines and photovoltaic by neural network technology
The power prediction of generating.
The object function of on-line optimization model in the step (2)：Object function f1Exchanged for micro-capacitance sensor with power distribution network
Power deviation is minimum, to ensure that micro-capacitance sensor turns into the stable power supply or load of power distribution network, reduces the control difficulty of power distribution network.
Under micro-capacitance sensor off-network state, it is 0 to set micro-capacitance sensor to exchange power with power distribution network.Object function f2The last period lotus at the beginning of energy-storage battery
The difference of electricity condition is minimum, to ensure that energy-storage battery has higher electricity.
minf2=SOC (1)-SOC (N)
Wherein, N is when hop count total in optimization time domain；Pl(i) it is the load power in period i；Ptie(i) in period i
Micro-capacitance sensor exchanges power with power distribution network, and on the occasion of representing micro-capacitance sensor to power distribution network injecting power, negative value represents power distribution network to micro- electricity
Net injecting power；Pw(i) exerted oneself for plan of the Wind turbines in period i；PPV(i) it is photovoltaic generation being planned out in period i
Power；Pb(i) exerted oneself for plan of the energy-storage battery in period i, the value is on the occasion of the value is negative value during electric discharge during charging；SOC
(1), SOC (N) is the 1st period, the energy-storage battery state-of-charge of N periods.
The constraints of on-line optimization model in the step (2)：Exerted oneself including Wind turbines, photovoltaic generation exert oneself with
And energy-storage battery discharge and recharge count constraint.
0 ＜ Pw(i) ＜ Pw_pre(i)
0 ＜ PPV(i) ＜ PPV_pre(i)
Nch_dis＜ Nbattery
Wherein, Pw_pre(i) exerted oneself predicted value for the Wind turbines in period i；Ppv_pre(i) it is the photovoltaic generation in period i
Exert oneself predicted value；Nch_disFor energy-storage battery discharge and recharge number of times；NbatteryFor the maximum allowable discharge and recharge number of times of energy-storage battery, typically
It is set to 1.
The derivation algorithm of on-line optimization model in the step (2)：The Optimized model includes 2 object functions, belongs to
Multi-objective optimization question.Multi-objective optimization algorithm has 3 main Performance Evaluating Indexes：1. the solution tried to achieve is as far as possible close
Pareto optimal solutions；2. the distributivity and diversity of solution colony are kept as far as possible；3. to prevent what is obtained in solution procedure
Pareto optimal solutions are lost.Correspondingly, nondominated sorting genetic algorithm II (NSGA- II) has 3 passes
Key technology becomes a kind of outstanding multi-objective optimization algorithm, i.e., quick non-dominated ranking, individual crowding distance and elite plan
Slightly, it is therefore of the invention using the Algorithm for Solving on-line optimization models of NSGA- II.
Real-time, tunable capacity in the step (3)：The on-line optimization stage, its lead was larger relative to the control moment,
Predicated error is also larger, and the Wind turbines and photovoltaic generation that its optimization is come could possibly be higher than Wind turbines and light with reference to power curve
The actual EIAJ generated electricity is lied prostrate, leads to not accurate completion and refers to power curve, add the power back-off pressure of energy-storage battery
Power.In order to improve Wind turbines and photovoltaic generation with reference to the completeness exerted oneself, sent out in control moment according to Wind turbines and photovoltaic
The real-time, tunable capacity of electricity, to the two with reference to carry out feedback adjustment of exerting oneself, so that energy-storage battery is as far as possible according to on-line optimization
Curve motion.
Wind turbines real-time, tunable capacity △ PwFor：
ΔPw=Pw_est-Pw
Wherein, PwExerted oneself for Wind turbines reference；Pw_estExerted oneself in real time estimate for Wind turbines, the estimate can pass through
Real-time wind speed and fan operation state are quickly calculated.
Photovoltaic generation real-time, tunable capacity △ PpvFor：
ΔPpv=Ppv_est-Ppv
Wherein, PpvExerted oneself for photovoltaic generation reference；Ppv_estExerted oneself in real time estimate for photovoltaic generation, the estimate can lead to
Real-time lighting intensity and temperature etc. is crossed quickly to calculate.
Feedback adjustment in the step (3)：Variable capacity, feedback adjustment ring are whether there is according to Wind turbines and photovoltaic generation
Section specifically includes following 4 kinds of situations.
(1) Wind turbines and photovoltaic generation are respectively provided with variable capacity, you can capacitance-adjustable is that just, will now refer to and exert oneself directly
It is handed down to Wind turbines, photovoltaic generation and energy-storage battery.
(2) Wind turbines and photovoltaic generation are without variable capacity, you can capacitance-adjustable is negative, will now be referred under exerting oneself directly
Issue Wind turbines and photovoltaic generation, energy-storage battery compensation power shortage.
(3) Wind turbines have variable capacity, and photovoltaic generation is without variable capacity, and photovoltaic generation has superfluous plan, now
Photovoltaic generation surplus plan is transferred to Wind turbines, adjustment is as follows with reference to exerting oneself：
Pw_sch=Pw+min(ΔPw,-ΔPpv)
Ppv_sch=Ppv-min(ΔPw,-ΔPpv)
Wherein, Pw_schExerted oneself for Wind turbines last minute planning；Ppv_schExerted oneself for photovoltaic generation last minute planning.
(4) photovoltaic generation has variable capacity, and Wind turbines are without variable capacity, and Wind turbines have superfluous plan, now
Wind turbines surplus plan is transferred to photovoltaic generation, adjustment is as follows with reference to exerting oneself：
Ppv_sch=Ppv+min(ΔPpv,-ΔPw)
Pw_sch=Pw_est-min(ΔPpv,-ΔPw)。
According to the micro-capacitance sensor wind-light storage model predictive control method flow shown in Fig. 1, micro-capacitance sensor wind-light storage model has been worked out
Predictive control algorithm realizes program.Test parameter sets as follows in embodiment：Fan capacity is 66kW, and photovoltaic capacity is 200kW,
Energy storage is 90kW/270kWh, and load capacity is 120kW, and it is 60kW that power distribution network and micro-capacitance sensor, which exchange power, and energy-storage battery is initial
SOC is 0.5.
The prediction EIAJ of Wind turbines and photovoltaic generation and actual EIAJ curve difference are as shown in Figures 2 and 3.
The predicted value of wind power is bigger than normal, and actual EIAJ is more than within all the period of time.Predicting the outcome for photovoltaic generation can be preferable
The change of identical actual EIAJ, precision of prediction is of a relatively high.
Hop count is 15 when setting on-line optimization, a length of 1 minute during each period, and optimization total duration is 15 minutes.Using
The algorithms of NSGA- II are optimized, and population number is 400, and algebraically is 200.Optimized algorithm is time-consuming 10 seconds 1 minute, disclosure satisfy that online
The requirement of control.Typical Pareto prioritization schemes are as shown in table 1.
The typical case's Pareto prioritization schemes of table 1
| f1/kW | f2 | |
| Scheme 1 | 0.1613 | 0.4907 |
| Scheme 2 | 2.7910 | 0.6017 |
With object function f1For main preference, object function f2For secondary preference, selection scheme 1 is optimum control scheme.Side
The optimization of Wind turbines, photovoltaic generation and energy-storage battery is as shown in Figure 4 with reference to power curve in case 1.
The reference of Wind turbines and photovoltaic generation is exerted oneself according to conditions such as real-time wind speed, illumination and temperature and adjusted
Whole, the reference power curve after adjustment is as shown in figure 5, power surplus plan is transferred to photovoltaic generation by Wind turbines.
Wind turbines and photovoltaic generation exert oneself in real time can be preferably after tracking adjustment reference exert oneself, as shown in Figure 6.
The reference power curve that energy-storage battery can give according to on-line optimization substantially is run, and effectively reduces the power of energy-storage battery
Pressure is compensated, energy-storage battery discharge process is as shown in Figure 7.
Although above-mentioned the embodiment of the present invention is described with reference to accompanying drawing, not to present invention protection model
The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
Need to pay various modifications or deform still within protection scope of the present invention that creative work can make.
Claims (8)
1. a kind of micro-capacitance sensor wind-light storage model predictive control method, it is characterized in that：Comprise the following steps：
(1) forecast model is set up, passes through Wind turbines and photovoltaic generation in micro-capacitance sensor in the forecast model prediction following setting period
EIAJ；
(2) using the wind-powered electricity generation and photovoltaic EIAJ that predict as constraints, to Wind turbines, photovoltaic generation in micro-capacitance sensor with
And energy-storage battery three exerts oneself carry out on-line optimization, the reference for providing three is exerted oneself；
(3) according to Wind turbines and the real-time, tunable capacity of photovoltaic generation, to Wind turbines, photovoltaic generation and energy-storage battery three
Person is with reference to carry out feedback adjustment of exerting oneself；
Feedback adjustment in the step (3)：Variable capacity, feedback adjustment link tool are whether there is according to Wind turbines and photovoltaic generation
Body includes following 4 kinds of situations：
(a) Wind turbines and photovoltaic generation are respectively provided with variable capacity, you can capacitance-adjustable directly issue for that just, now will refer to exert oneself
To Wind turbines, photovoltaic generation and energy-storage battery；
(b) Wind turbines and photovoltaic generation are without variable capacity, you can capacitance-adjustable is negative, now will be directly handed down to reference to exerting oneself
Wind turbines and photovoltaic generation, energy-storage battery compensation power shortage；
(c) Wind turbines have variable capacity, and photovoltaic generation is without variable capacity, and photovoltaic generation has superfluous plan, now by light
The superfluous plan of volt generating is transferred to Wind turbines；
(d) photovoltaic generation has variable capacity, and Wind turbines are without variable capacity, and Wind turbines have superfluous plan, now by wind
Group of motors surplus plan is transferred to photovoltaic generation.
2. a kind of micro-capacitance sensor wind-light storage model predictive control method as claimed in claim 1, it is characterized in that：The step (1)
In, forecast model realizes the power prediction of Wind turbines and photovoltaic generation by neural network technology, according to going through for process
The following output valve of history information and following input prediction process.
3. a kind of micro-capacitance sensor wind-light storage model predictive control method as claimed in claim 1, it is characterized in that：The step (2)
In on-line optimization model object function：Object function f1Power deviation minimum is exchanged with power distribution network for micro-capacitance sensor, to ensure
Micro-capacitance sensor turns into the stable power supply or load of power distribution network, reduces the control difficulty of power distribution network, under micro-capacitance sensor off-network state, if
It is 0, object function f to put micro-capacitance sensor and power is exchanged with power distribution network2The difference of last period state-of-charge is minimum at the beginning of energy-storage battery, to protect
Demonstrate,proving energy-storage battery has higher electricity；
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min f2=SOC (1)-SOC (N)
Wherein, N is when hop count total in optimization time domain；Pl(i) it is the load power in period i；Ptie(i) it is micro- electricity in period i
Net exchanges power with power distribution network, and on the occasion of representing micro-capacitance sensor to power distribution network injecting power, negative value represents that power distribution network is noted to micro-capacitance sensor
Enter power；Pw(i) exerted oneself for plan of the Wind turbines in period i；PPV(i) exerted oneself for photovoltaic generation in period i plan；Pb
(i) exerted oneself for plan of the energy-storage battery in period i, the value is on the occasion of the value is negative value during electric discharge during charging；SOC(1)、SOC
(N) it is the 1st period, the energy-storage battery state-of-charge of N periods.
4. a kind of micro-capacitance sensor wind-light storage model predictive control method as claimed in claim 1, it is characterized in that：The step (2)
In on-line optimization model constraints：Exerted oneself including Wind turbines, photovoltaic generation is exerted oneself and energy-storage battery discharge and recharge time
Number constraint：
0 ＜ Pw(i) ＜ Pw_pre(i)
0 ＜ PPV(i) ＜ PPV_pre(i)
Nch_dis＜ Nbattery
Wherein, Pw_pre(i) exerted oneself predicted value for the Wind turbines in period i；Ppv_pre(i) exerted oneself for the photovoltaic generation in period i
Predicted value；Nch_disFor energy-storage battery discharge and recharge number of times；NbatteryFor the maximum allowable discharge and recharge number of times of energy-storage battery, Pw(i) it is wind
Plan of the group of motors in period i is exerted oneself；PPV(i) exerted oneself for photovoltaic generation in period i plan.
5. a kind of micro-capacitance sensor wind-light storage model predictive control method as claimed in claim 1, it is characterized in that：The step (2)
In the derivation algorithm specific method of on-line optimization model be：The Optimized model includes 2 object functions, belongs to multiple-objection optimization
Problem, multi-objective optimization algorithm has 3 Performance Evaluating Indexes：1. the solution tried to achieve will try one's best close to Pareto optimal solutions；2.
The distributivity and diversity of solution colony are kept as far as possible；3. to prevent the Pareto optimal solutions obtained from losing in solution procedure, use
The Algorithm for Solving on-line optimization models of NSGA- II.
6. a kind of micro-capacitance sensor wind-light storage model predictive control method as claimed in claim 1, it is characterized in that：The step (3)
In the specific method of real-time, tunable capacity be：Wind turbines real-time, tunable capacity △ PwFor：
ΔPw=Pw_est-Pw
Wherein, PwExerted oneself for Wind turbines reference；Pw_estExerted oneself in real time estimate for Wind turbines, the estimate can be by real-time
Wind speed and fan operation state are quickly calculated；
Photovoltaic generation real-time, tunable capacity △ PpvFor：
ΔPpv=Ppv_est-Ppv
Wherein, PpvExerted oneself for photovoltaic generation reference；Ppv_estExerted oneself in real time estimate for photovoltaic generation, the estimate passes through real-time
Intensity of illumination and temperature computation come out.
7. a kind of micro-capacitance sensor wind-light storage model predictive control method as claimed in claim 6, it is characterized in that：The step (c)
In, with reference to exerting oneself, adjustment is as follows：
Pw_sch=Pw+min(ΔPw,-ΔPpv)
Ppv_sch=Ppv-min(ΔPw,-ΔPpv)
Wherein, Pw_schExerted oneself for Wind turbines last minute planning；Ppv_schExerted oneself for photovoltaic generation last minute planning, △ PpvSent out for photovoltaic
Electric real-time, tunable capacity, PwExerted oneself for Wind turbines reference, PpvExerted oneself for photovoltaic generation reference.
8. a kind of micro-capacitance sensor wind-light storage model predictive control method as claimed in claim 6, it is characterized in that：The step (d)
In, with reference to exerting oneself, adjustment is as follows：
Ppv_sch=Ppv+min(ΔPpv,-ΔPw)
Pw_sch=Pw_est-min(ΔPpv,-ΔPw)
Wherein, Pw_schExerted oneself for Wind turbines last minute planning；Ppv_schExerted oneself for photovoltaic generation last minute planning, △ PpvSent out for photovoltaic
Electric real-time, tunable capacity, PwExerted oneself for Wind turbines reference, PpvExerted oneself for photovoltaic generation reference.
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