CN108256736A - The power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor - Google Patents
The power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor Download PDFInfo
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Abstract
The invention discloses a kind of power generation dispatching technologies of time correlation regenerative resource involved in micro-capacitance sensor, are mainly used for solving the high cost problem that the energy generates in micro-capacitance sensor.The characteristics of in order to solve the randomness of solar power generation, discontinuity, unstability; the present invention devises a flexible ambiguous model of novelty; mean value and second moment information are obtained by the historical data of solar power generation amount; thus a uncertain collection of square statistics is defined to limit the fluctuation of the true generated energy of solar energy; and consider the temporal correlation of solar power generation amount; finally design energy-efficient power generation dispatching strategy; the use of battery is as much as possible customer power supply; in the hope of consuming the minimum electricity charge, achieve the purpose that energy saving and environmental protection.
Description
Technical field
The invention belongs to field of renewable energy technology, and in particular to time correlation renewable energy involved in a kind of micro-capacitance sensor
The power generation dispatching technology in source.
The invention discloses a kind of power generation dispatching technologies of time correlation regenerative resource involved in micro-capacitance sensor, are mainly used for
Solve the high cost problem that the energy generates in micro-capacitance sensor.The present invention considers the two major features of micro-capacitance sensor, first, being integrated with big rule
The regenerative resource of mould deploys a large amount of solar photovoltaic cell panel, can be by solar powered, second is that being equipped with storing up electricity system
It unites (battery), you can to store the generated energy of solar energy, and the electricity of solar energy output will be preferably battery charging, in addition to electricity
Pond, micro-capacitance sensor can also be powered by the generator that itself is equipped with for domestic consumer.Micro-capacitance sensor must be supplied to economic and reliable for user
Electricity will also minimize cost of electricity-generating to meet user demand by rational power generation dispatching technology.
Background technology
Power grid passes through largely to be recombinated using distributed generator, so that power generation is more environmentally-friendly and economical.With micro-
The basic structure of power grid " plug and play " is popularized, and following power grid will also develop and develop towards this direction.Micro-capacitance sensor can be grid-connected
Mode is run, that is, is allowed from power grid input electric power, can also isolated island mode run, i.e., be isolated with upstream power grid, and when needed
As power supply it is customer power supply by the use of its local generator.
With being skyrocketed through for people's electricity consumption, realize that the production of energy scheduling of the supply of electric power of economic and reliable becomes
The important component of micro-capacitance sensor.The two major features of micro-capacitance sensor are the utilizations of extensive regenerative resource and equipped with energy storage system
System, however, these features while tremendous economic and environmental benefit is brought to micro-capacitance sensor, also give micro-capacitance sensor Intelligent Control Strategy
Design bring huge challenge.Traditional power generation dispatching scheme is typically based on the very predictable of energy generation, i.e., traditional
Grid generation amount number be predictable and controllable, and this is highly unstable and be difficult to pre- for using regenerative resource
During the generation mode of survey, traditional power generation dispatching scheme has no ample scope for abilities.Although energy storage integrated technology can be to a certain degree
The upper uncertain problem that brings of fluctuation for alleviating regenerative resource, but the traffic control of whole system or very complicated.By
In these unique challenges, it is still one to be designed steadily and surely for micro-capacitance sensor and have high cost-benefit production of energy scheduling scheme
Outstanding question.
In order to mitigate micro-capacitance sensor depending on unduly and reducing environmental pollution to traditional power grid, regenerative resource is advised greatly
Mould is integrated into micro-capacitance sensor, such as solar power generation.Due to the generated energy of solar energy and weather, temperature, time and geographical location
Etc. related, there is very big fluctuation and randomness, for the unstable characteristic of solar power generation, handle solar energy hair both at home and abroad
The mode of this stochastic variable of electricity mainly has two major class, and one kind is random optimization, and this method needs to obtain stochastic variable in advance
Distribution function, then constantly sampling solves, time-consuming and laborious, is suitble to calculation amount small and simple scene, and in actual scene, very
The difficult accurate distribution function for obtaining stochastic variable；Another kind of is robust optimization, and this method biggest advantage is not need to become at random
The information of distribution function is measured, but by defining a uncertain collection, stochastic variable can be in one defined range of uncertain collection
Interior fluctuation, it is contemplated that the optimal solution under worst case to the full extent close to actual scene, has very big flexibility and can
Control property.
The purpose of the present invention and meaning are that the above-mentioned advantage optimized using robust is become the introducing of solar energy by ideal
For reality, by the processing to this stochastic variable of solar power generation amount, it is at random determining to change so that micro-capacitance sensor no longer individually according to
Rely in traditional power grid, on this basis using energy-efficient power generation dispatching technology, solar energy generation technology and micro-capacitance sensor are matched in itself
Standby electrical power generators technology organically combines, and is that the power generation process of micro-capacitance sensor formulates energy-saving and environment-friendly scheduling scheme.With
Previous technology is compared, and the maximum advantage of the present invention is exactly not need to know in advance the specific distribution function of solar power generation amount,
But by the mean value of historical data and second moment information, a uncertain collection of square statistics is defined to limit solar power generation amount
The fluctuation being really distributed, the uncertain collection not only include many details of solar power generation amount, it is also contemplated that solar energy
Then the temporal correlation of generated energy is converted and is solved the son of solar power generation amount by chance constraint and robust Optimal methods
Problem has solved the integer programming primal problem for minimizing cost of electricity-generating finally by branch and bound method.By collecting truthful data
It being tested, the results showed that the present invention can carry out the robustness of control system with the elasticity that dynamic regulation battery charging and discharging constrains,
The constraint is more loose, and the total power production cost of micro-capacitance sensor is lower, and the quantity of the temporal correlation of solar power generation amount is also to power generation
Cost has an impact, and optimizing decision is to formulate scheduling strategy using the time related information of solar power generation amount in 8 hours.It is comprehensive
Upper described, the present invention can provide some reference propositions to build the micro-grid system of a green energy conservation conscientiously.
Invention content
Goal of the invention：In order to overcome the deficiencies in the prior art, the present invention provides the time involved in a kind of micro-capacitance sensor
The power generation dispatching technology of related regenerative resource, mainly can be again using time correlation involved in robust optimisation technique solution micro-capacitance sensor
The power generation dispatching problem of the raw energy.
Technical solution：To achieve the above object, the technical solution adopted by the present invention mainly includes the following contents：
1) acquisition of solar power generation amount
At present, micro-capacitance sensor is mostly run in a manner of isolated island, i.e., the generator being equipped with using itself for customer power supply, do not need to according to
Rely in traditional power grid, while the battery in micro-capacitance sensor is alternatively user and is powered.It, when carrying out energy scheduling to micro-capacitance sensor
The charge volume of battery is asked to be not less than the discharge capacity of battery, in of the invention, the charge volume of battery comes from the generated energy of solar energy, and
The generated energy of solar energy is a stochastic variable, and therefore, the processing to the variable is the key job in entire model.Due to too
The discontinuity and unstability that sun can generate electricity, it is difficult to its accurate probability distribution is obtained, but a large amount of historical data provides again
Therefore effective information about solar power generation amount, can obtain solar energy hair from these historical datas and in-site measurement
The mean value and second moment of electricity do not know to collect to limit the fluctuation being really distributed, and pass through regulating cell and fill so as to define one
Discharge constraint the elasticity dynamic control process robustness, then, the charge and discharge constraint of battery passes through chance constraint and wide
Adopted Chebyshev inequality is converted into a semi definite programming subproblem, is optimized by robust, interior point method and dichotomy can obtain
Obtain the optimal solution of the subproblem, i.e., the solar power generation amount under worst case.
2) energy-saving power generation dispatching strategy
Maintenance cost and power generation when the cost of electricity-generating of micro-capacitance sensor mainly includes the cost of electricity-generating of generator, generator is run
The start-up cost of machine, the electricity of micro-capacitance sensor supply user can jointly be provided by generator and battery, wherein, the charge volume of battery comes from
In the generated energy of solar energy.In order to minimize the cost of electricity-generating of micro-capacitance sensor, it is necessary to meet user power utilization demand and entire power generation
In period, one energy-efficient power generation dispatching strategy of design solves the problems, such as three following：A. how many generator is in running order；
B. each in running order generator needs generate how many electricity；C. solar panel needs generate how many electricity and charge for battery.
After abovementioned steps obtain solar power generation amount, the master for minimizing micro-capacitance sensor cost of electricity-generating can be solved by branch and bound method
Problem, the cost of electricity-generating for ensureing to make in the case where meeting user power utilization demand micro-capacitance sensor are minimum.
A kind of power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor, customer power supply source include battery
The conventional electric generators being equipped with micro-capacitance sensor itself, generated energy of the reserve of electricity from solar energy of the battery are a stochastic variable；
The stochastic variable is handled by the method that chance constrained programming and robust optimize, primal problem is become not contain the half of stochastic variable
Positive definite plans the subproblem of form, then carries out primal problem solution, the entitled energy for minimizing entire micro-grid system of the examination in chief
Dispatch cost, that is, total electricity bill；Specifically include following steps：
(1) historical data of solar power generation amount is obtained；
(2) mean value and second moment information are obtained according to historical data；
(3) fluctuation being really distributed using mean value and the uncertain collection limitation solar power generation amount of second-order moments square statistics；
(4) charge and discharge of battery constraint is converted into chance constraint, robust optimization for forming semi definite programming form is asked
Topic；
(5) it is converted by Chebyshev inequality, the solar power generation amount under interior point method, dichotomy acquisition worst caseStochastic variable is converted into determining parameter；
At this point, the charge and discharge constraint of battery no longer contains stochastic variable, turn toWherein, H represents the entire energy
Dispatching cycle, VhRepresent electricity of the battery in h-th of time slot supply user, i.e. discharge capacity；
(6) when obtaining the solar power generation amount under worst case, corresponding minimum total electricity bill.
Further, the charge and discharge of the battery are constrained to：The discharge capacity of electric energy is less than or equal to charge volume, i.e.,The electricity consumption of discharge capacity, that is, user, the generated energy of charge volume, that is, solar energy, wherein, H represents the entire energy
Dispatching cycle, VhRepresent electricity of the battery in h-th of time slot supply user, i.e. discharge capacity, ξhRepresent solar energy in h-th of time slot
Generated energy, i.e., to the charge volume of battery.
Further, the charge capacity of battery is constrained to：The charge capacity of battery is a variable related with the time, and adjacent
Time slot has relevance, is expressed as Bh+1=Bh+ξh-Vh, wherein, Bh+1Represent the battery charge capacity that the h+1 time slot starts,
The battery charge capacity at i.e. h-th time slot end, VhRepresent electricity of the battery in h-th of time slot supply user, i.e. discharge capacity, ξhIt represents
Solar energy h-th of time slot generated energy, i.e., to the charge volume of battery, and 0≤Bh≤Bmax, wherein, BmaxRepresent battery most
Big charge capacity.
Further, the total electricity bill of micro-capacitance sensor is made of three parts：The dimension when cost of electricity-generating of generator, generator operation
Cost and the start-up cost of generator are protected, mathematic(al) representation isWherein, H tables
Showing the entire energy scheduling period, A represents generator set,Represent the marginal cost of generator unit generated energy,Represent hair
Motor a is in the generated energy of h-th of time slot, satisfactionWherein,Minimum power generation for generator a
Amount,For the maximum generating watt of generator a,Represent that generator a is a bi-values in the working condition of h-th of time slot,
It represents that generator is in running order when being 0, represents that generator is closed when being 1,Represent generator a in h
The cost of electricity-generating of a time slot,Represent the maintenance cost of unit interval when generator a is in running order,Represent power generation
Machine a h-th of time slot operation expense,Represent the start-up cost of generator a,Represent generator a
In the start-up cost of h-th of time slot.
Further, the method for minimizing the energy scheduling cost of entire micro-grid system is：Generator powered is reduced, is increased
Add and battery charging is powered using solar power generation, i.e.,
Wherein, A represents generator set,Represent generator a in the generated energy of h-th of time slot, VhRepresent battery in h
The electricity of a time slot supply user, i.e. discharge capacity, DhRepresent user in the power demand of h-th of time slot, ξhRepresent solar energy the
The generated energy of h time slot, i.e., to the charge volume of battery；X=[x1,x2,...,xa,...]T, Y=[y1,y2,...,ya,...]TWith
V=[V1,V2,...,Vh...] and it is decision variable x respectivelya、yaAnd VhMatrix, ()+Represent max function, i.e., (x)+=
Max (0, x), so far, primal problem just forms.
Further, correlation variableProcessing method be：Introduce an auxiliary variableI.e. problem (1)-
(5) become
Wherein, Z|A|×HIt is auxiliary variableMatrix, so far, problem (6)-(10) are without correlation variable.
Further, the method for the uncertain collection of square statistics is defined in step (3)：Pass through the history number of solar power generation amount
According to mean value and second moment is obtained, the fluctuation of solar power generation amount is portrayed by following uncertain collection：
Wherein, μ is the mean value of solar power generation amount, and S is the second moment of solar power generation amount, comprising sun generated energy in phase
The correlation of adjacent time slot, PξAn element for ξ distribution spaces.
Further, the method for chance constraint conversion is in step (4)：Battery charging and discharging is constrained toWherein, the ξ of solar energy yield is representedhIt is a stochastic variable, which is converted into chance constraint：
I.e.
Wherein, ε is an Error Tolerance factor, represents the conservative degree of the constraint.
Further, the method for foundation and the solution of semi definite programming model is：By the conversion of Chebyshev inequality,
The left side of inequality is converted into the Robust Optimization Model of a semi definite programming form in above-mentioned constraint, i.e. son in primal problem is asked
Topic：
Wherein,[a2,...,aH+1]T=-1IH, [aH+2,...,a2H+1]T=IHAnd IHIt is tieed up for H
Unit matrix,Represent robustness electric energy acquisition amount, i.e., the electricity obtained from battery within entire dispatching cycle is
One robustness threshold value, [b2,...,bH+1]=[0 ..., 0]T, [bH+2,...,b2H+1]=ξmax·[1,1,...,1]TAnd k=
2H+1, λiIt is a decision variable；
DefinitionFor the fault tolerant probability under worst case, pass through interior point
The optimal solution of the model can be obtained in method, then can acquire satisfaction by dichotomySolution, algorithm flow design such as
Under：
1) the Error Tolerance ε that input-mean μ, second moment S, search radius ρ, battery charging and discharging constrain；
2) initial ranging section [0, ρ] is defined；
3) the optimal solution K of subproblem is solved with interior point methodξ(b1)；
4) it is solved with dichotomyThenSolar power generation amount as under worst case is one and determines
Value, output
So far, as the subproblem of primal problem, which has been obtained the solar energy yield under worst case, stochastic variable
Become determining variable, the inequality constraints (9) in primal problem is converted into
Further, primal problem becomes the mixed integer programming for not containing stochastic variable, by include branch and bound method,
Method including Surface by Tangent Plane Method solves.
The power generation dispatching technology of time correlation regenerative resource, innovative point are involved in the micro-capacitance sensor of the present invention：
(1) micro-capacitance sensor can be customer power supply by power storage systems such as the conventional electric generators itself being equipped with and batteries；The present invention is
Alleviate the significant cost of micro-capacitance sensor power generation and mitigate environmental pollution, pass through conventional electric generators and battery that micro-capacitance sensor itself is equipped with
Power storage systems are waited as customer power supply, generated energy of the reserve of electricity from solar energy of battery.
(2) the manipulative randomness of solar power generation amount；
(3) rational energy-saving power generation strategy is devised, the electricity charge are minimized to customer power supply using battery as much as possible.
Advantageous effect：The power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor provided by the invention, with
The prior art is compared, and is had the advantage that：The present invention is handled solar power generation amount using the method for robust optimization, then
Rational energy-saving power generation dispatching technology is devised, the utilization rate of solar energy is ensure that, effectively reduces cost of electricity-generating, reduce carbon
Discharge capacity has achieved the purpose that energy-saving and emission-reduction.
Description of the drawings
The system architecture diagram of Fig. 1 micro-capacitance sensors；
Fig. 2 solves the algorithm pattern of subproblem；
Fig. 3 solves the flow chart of primal problem；
The temporal correlation truthful data fitted figure of Fig. 4 solar power generation amounts；
Fig. 5 Error Tolerances ε is to robustness threshold valueInfluence；
Influences of Fig. 6 Error Tolerances ε to micro-capacitance sensor cost of electricity-generating；
Robustness threshold values different Fig. 7The temporal correlation quantity of lower solar power generation amount is to error probability Kξ(b1)
It influences.
Specific embodiment
The invention discloses a kind of power generation dispatching technologies of time correlation regenerative resource involved in micro-capacitance sensor, are mainly used for
Solve the high cost problem that the energy generates in micro-capacitance sensor.The present invention considers the two major features of micro-capacitance sensor, first, being integrated with big rule
The regenerative resource of mould deploys a large amount of solar photovoltaic cell panel, can be by solar powered, second is that being equipped with storing up electricity system
It unites (battery), you can the generated energy of solar energy is stored, and the electricity of solar energy output will be preferably battery charging, in addition to electricity
Pond, micro-capacitance sensor can also be powered by the generator of itself for domestic consumer.In order to solve the randomness of solar power generation, interruption
The characteristics of property, unstability, the present invention devises a flexible ambiguous model of novelty, passes through the history of solar power generation amount
Thus data acquisition mean value and second moment information define a uncertain collection of square statistics to limit the true generated energy of solar energy
Fluctuation, and the temporal correlation of solar power generation amount is considered, energy-efficient power generation dispatching strategy is finally designed, it is as more as possible
Ground is customer power supply using battery, in the hope of consuming the minimum electricity charge, achievees the purpose that energy saving and environmental protection.
The present invention is further described with reference to the accompanying drawings and examples.
Embodiment
As shown in Figure 1, micro-capacitance sensor by traditional power supply unit, renewable energy system (such as solar panel),
Power storage system (such as battery) composition, user can not only be powered by traditional power supply unit, but also can be battery powered, the electricity of battery
Measure the generated energy from solar energy.
1. the electric energy demand and supply constraint of micro-capacitance sensor
In the present invention, for micro-capacitance sensor by user demand to customer power supply, i.e. the power supply volume of micro-capacitance sensor is equal to the electricity consumption of user,
Wherein, micro-capacitance sensor is equal to the discharge capacity in the generated energy power-up pond of generator in the power supply volume of each time slot, i.e.,Wherein, A represents generator set,Represent generator a in the generated energy of h-th of time slot, VhRepresent battery
In the electricity of h-th of time slot supply user, i.e. discharge capacity, DhRepresent power demand of the user in h-th of time slot.
2. the battery constraint of micro-capacitance sensor
The charge and discharge constraint of step 2.1 battery：User can be also battery powered by the generator powered that micro-capacitance sensor carries,
And generated energy of the charge volume of battery from solar energy, in order to ensure that Emergency time battery has electricity, the discharge capacity of battery must be small
In equal to charge volume, i.e.,Wherein, H represents entire energy scheduling period, VhRepresent battery at h-th
Gap supplies the electricity of user, i.e. discharge capacity, ξhRepresent generated energy of the solar energy in h-th of time slot, i.e., to the charge volume of battery；
The charge capacity constraint of step 2.2 battery：The charge capacity of battery is a variable related with the time, and adjacent time-slots
With relevance, B can be expressed ash+1=Bh+ξh-Vh, wherein, Bh+1Represent the battery charge capacity that the h+1 time slot starts,
The battery charge capacity at i.e. h-th time slot end, VhRepresent electricity of the battery in h-th of time slot supply user, i.e. discharge capacity, ξhIt represents
Solar energy h-th of time slot generated energy, i.e., to the charge volume of battery.Finally, the charge capacity of battery must be non-negative and cannot be surpassed
Cross its maximum charge capacity, i.e. 0≤Bh≤Bmax, wherein, BmaxRepresent the maximum charge capacity of battery.
3. the electricity charge model of micro-capacitance sensor
The total electricity bill of micro-capacitance sensor is made of three parts：The maintenance cost when cost of electricity-generating of generator, generator operation and
The start-up cost of generator, mathematic(al) representation areWherein, H represents entire energy
Source dispatching cycle, A represent generator set,Represent the marginal cost of generator unit generated energy,Represent generator a the
The generated energy of h time slot meetsWherein,For the minimum generated energy of generator a,For
The maximum generating watt of generator a,It represents that generator a is a bi-values in the working condition of h-th of time slot, represents to send out for 0
Motor is in running order, is closed for 1 expression generator,Represent power generations of the generator a in h-th of time slot
Cost,Represent the maintenance cost of unit interval when generator a is in running order,Represent generator a at h-th
The operation expense of gap,Represent the start-up cost of generator a,Represent generator a in h-th time slot
Start-up cost.
4. primal problem is formed
In order to minimize the energy scheduling cost of entire micro-grid system, it is necessary to reduce user to generator powered according to
Rely, the use of solar power generation to battery charging and then is as much as possible customer power supply, i.e.,
Wherein, X=[x1,x2,...,xa,...]T, Y=[y1,y2,...,ya,...]TWith V=[V1,V2,...,Vh,...]
It is decision variable x respectivelya、yaAnd VhMatrix, ()+Represent max function, i.e., (x)+=max (0, x), so far, primal problem
Just it forms.
Step 4.1 correlation variableProcessing：One intractable part of primal problem (1)-(5) is correlation
Property variableIt deals with cumbersome, therefore an auxiliary variable can be introducedI.e. problem (1)-(5) become
Wherein, Z|A|×HIt is auxiliary variableMatrix, so far, problem (6)-(10) just be free of correlation variable.
4th, the processing of the stochastic variable of subproblem
The generated energy of solar energy is a stochastic variable for having close association with time, weather, temperature, have it is unstable,
Discontinuity feature is dealt with more complicated.The present invention obtains solar power generation amount by the processing to historical data and field measurement
Mean value and second moment information, define square statistics uncertain collection to limit the fluctuation range of this stochastic variable, specifically
It is described as follows：
Step 4.1 defines the uncertain collection of square statistics：Mean value and second order can be obtained by the historical data of solar power generation amount
Square, so as to portray the fluctuation of solar power generation amount by following uncertain collection：
Wherein, μ is the mean value of solar power generation amount, and S is the second moment of solar power generation amount, comprising sun generated energy in phase
The correlation of adjacent time slot；
Step 4.2 chance constraint converts：The battery charging and discharging that step 2.1 is mentioned is constrained toWherein,
Represent the ξ of solar energy yieldhIt is a stochastic variable, which is converted into chance constraint by the present invention：
I.e.
Wherein, ε is an Error Tolerance factor, represents the conservative degree of the constraint, and ε is bigger, guards and spends higher, robustness
It is stronger；
The foundation and solution of step 4.3 semi definite programming model：By the conversion of Chebyshev inequality, in above-mentioned constraint
The left side of inequality can be converted into the Robust Optimization Model of a semi definite programming form, which is also 3 Central Plains problems
(6) subproblem of-(10)：
Wherein,[a2,...,aH+1]T=-1IH, [aH+2,...,a2H+1]T=IHAnd IHIt is tieed up for H
Unit matrix,Represent robustness electric energy acquisition amount, i.e., the electricity obtained from battery within entire dispatching cycle is
One robustness threshold value, [b2,...,bH+1]=[0 ..., 0]T, [bH+2,...,b2H+1]=ξmax·[1,1,...,1]TAnd k=
2H+1.DefinitionFor the fault tolerant probability under worst case, pass through interior point method
The optimal solution of the model can be obtained, then satisfaction can be acquired by dichotomySolution, algorithm flow design it is as follows：
1) the Error Tolerance ε that input-mean μ, second moment S, search radius ρ, battery charging and discharging constrain；
2) initial ranging section [0, ρ] is defined；
3) the optimal solution K of subproblem is solved with interior point methodξ(b1)；
4) it is solved with dichotomyThenSolar power generation amount as under worst case is a determining value,
Output
Specific algorithm steps and details are shown in attached drawing 2.So far, the subproblem as problem in 3, which has been obtained the worst
In the case of solar energy yield, stochastic variable is become determining variable, the inequality constraints (9) in 3 is converted into
5th, primal problem solves
After handling solar power generation amount this stochastic variable well, the primal problem in 3, which becomes, does not contain the mixed of stochastic variable
Integer programming is closed, can see attached drawing 3 by the solutions such as branch and bound method, Surface by Tangent Plane Method, integrated solution flow.
6. a kind of embodiment example
(1) the solar cell plate suqare of micro-capacitance sensor deployment is 1.5 × 104m2, fine index monthly from new plus
10 weather stations on slope, present invention uses this 10 weather stations the first two week in November, 2012 solar radiation number
According to, and fitting is made that these truthful datas, as a result see attached drawing 4, color point is true correlation data, and blue line is fitting
Curve afterwards, expression formula rtc+ 0.00038 τ of=1-0.1644 τ2, wherein, τ represents time, rtcRepresent correlation, it can be seen that
The temporal correlation of solar power generation amount is substantially linear.
(2) present invention analyzes Error Tolerance factor ε to robustness threshold valueInfluence, as shown in Figure 5, ε withGrowth and increase and growth rate continuously decreases, this illustrates that Error Tolerance is bigger, and system gets over the dependence of solar energy yield
By force.
(3) present invention analyzes influences of the Error Tolerance factor ε to micro-capacitance sensor cost of electricity-generating, as shown in Figure 6, with
The increase of ε, cost of electricity-generating continuously decreases, this illustrates that Error Tolerance is bigger, and system is not guarded, and cost of electricity-generating is lower, and
And when Error Tolerance is very high, the reduction of cost of electricity-generating is no longer sensitive.
(4) present invention analyzes different robustness threshold valuesThe temporal correlation quantity of lower solar power generation amount is general to mistake
Rate Kξ(b1) influence, as shown in Figure 7, Kξ(b1) with b1Increase and increase, and with the increasing of temporal correlation quantity
Add, error probability is first reduced to be increased afterwards, this shows in a certain range the temporal correlation of the solar power generation amount of (within 8 hours)
Contribute to the conservative of reduction problem, the temporal correlation more than a certain range (other than 8 hours) is useless to problem decision even
It adversely affects.
The above is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (10)
1. a kind of power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor, it is characterised in that：Customer power supply comes
Source includes the conventional electric generators that battery and micro-capacitance sensor itself are equipped with, and generated energy of the reserve of electricity from solar energy of the battery is
One stochastic variable；The stochastic variable is handled by the method that chance constrained programming and robust optimize, primal problem is become not containing
The subproblem of the semi definite programming form of stochastic variable, then primal problem solution is carried out, the examination in chief is entitled to minimize entire micro- electricity
Energy scheduling cost, that is, total electricity bill of net system；Specifically include following steps：
(1) historical data of solar power generation amount is obtained；
(2) mean value and second moment information are obtained according to historical data；
(3) fluctuation being really distributed using mean value and the uncertain collection limitation solar power generation amount of second-order moments square statistics；
(4) charge and discharge of battery constraint is converted into chance constraint, forms the robust optimization subproblem of semi definite programming form；
(5) it is converted by Chebyshev inequality, the solar power generation amount under interior point method, dichotomy acquisition worst caseIt will
Stochastic variable is converted into determining parameter；
At this point, the charge and discharge constraint of battery no longer contains stochastic variable, turn toWherein, H represents entire energy scheduling
Period, VhRepresent electricity of the battery in h-th of time slot supply user, i.e. discharge capacity；
(6) when obtaining the solar power generation amount under worst case, corresponding minimum total electricity bill.
2. the power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor according to claim 1, feature
It is：The charge and discharge of the battery are constrained to：The discharge capacity of electric energy is less than or equal to charge volume, i.e.,Electric discharge
The amount i.e. electricity consumption of user, the generated energy of charge volume, that is, solar energy, wherein, H represents entire energy scheduling period, VhRepresent battery
In the electricity of h-th of time slot supply user, i.e. discharge capacity, ξhRepresent generated energy of the solar energy in h-th of time slot, i.e., to battery
Charge volume.
3. the power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor according to claim 1, feature
It is：The charge capacity of battery is constrained to：The charge capacity of battery is a variable related with the time, and adjacent time-slots have association
Property, it is expressed as Bh+1=Bh+ξh-Vh, wherein, Bh+1Represent the battery charge capacity namely h-th of time slot end that the h+1 time slot start
Battery charge capacity, VhRepresent electricity of the battery in h-th of time slot supply user, i.e. discharge capacity, ξhRepresent solar energy at h-th
The generated energy of time slot, i.e., to the charge volume of battery, and 0≤Bh≤Bmax, wherein, BmaxRepresent the maximum charge capacity of battery.
4. the power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor according to claim 1, feature
It is：The total electricity bill of micro-capacitance sensor is made of three parts：Maintenance cost and power generation when the cost of electricity-generating of generator, generator are run
The start-up cost of machine, mathematic(al) representation areWherein, H represents entire energy tune
The period is spent, A represents generator set,Represent the marginal cost of generator unit generated energy,Represent generator a at h-th
The generated energy of time slot meetsWherein,For the minimum generated energy of generator a,For power generation
The maximum generating watt of machine a,It represents that generator a is a bi-values in the working condition of h-th of time slot, power generation is represented when being 0
Machine is in running order, represents that generator is closed when being 1,Represent power generations of the generator a in h-th of time slot
Cost,Represent the maintenance cost of unit interval when generator a is in running order,Represent generator a at h-th
The operation expense of gap,Represent the start-up cost of generator a,Represent generator a in h-th time slot
Start-up cost.
5. the power generation dispatching technology of time correlation regenerative resource involved in the micro-capacitance sensor according to claim 1 or 4, special
Sign is：The method for minimizing the energy scheduling cost of entire micro-grid system is：Generator powered is reduced, increases and uses the sun
It can generate electricity and battery charging is powered, i.e.,
Wherein, A represents generator set,Represent generator a in the generated energy of h-th of time slot, VhRepresent battery at h-th
Gap supplies the electricity of user, i.e. discharge capacity, DhRepresent user in the power demand of h-th of time slot, ξhRepresent solar energy at h-th
The generated energy of time slot, i.e., to the charge volume of battery；X=[x1,x2,...,xa,...]T, Y=[y1,y2,...,ya,...]TAnd V=
[V1,V2,...,Vh...] and it is decision variable x respectivelya、yaAnd VhMatrix, ()+Represent max function, i.e., (x)+=max
(0, x), so far, primal problem just forms.
6. the power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor according to claim 5, feature
It is：Correlation variableProcessing method be：Introduce an auxiliary variableI.e. problem (1)-(5) become
Wherein, Z|A|×HIt is auxiliary variableMatrix, so far, problem (6)-(10) are without correlation variable.
7. the power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor according to claim 1, feature
It is：The method of the uncertain collection of square statistics is defined in step (3)：By the historical data of solar power generation amount obtain mean value and
Second moment portrays the fluctuation of solar power generation amount by following uncertain collection：
Wherein, μ is the mean value of solar power generation amount, and S is the second moment of solar power generation amount, comprising the sun generated energy when adjacent
The correlation of gap, PξAn element for ξ distribution spaces.
8. the power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor according to claim 1, feature
It is：The method of chance constraint conversion is in step (4)：Battery charging and discharging is constrained toWherein, it represents too
The ξ of positive energy yieldhIt is a stochastic variable, which is converted into chance constraint：
I.e.
Wherein, ε is an Error Tolerance factor, represents the conservative degree of the constraint.
9. the power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor according to claim 8, feature
It is：The method of foundation and the solution of semi definite programming model is：By the conversion of Chebyshev inequality, in above-mentioned constraint not
The left side of equation is converted into the Robust Optimization Model of a semi definite programming form, i.e. subproblem in primal problem：
Wherein,[a2,...,aH+1]T=-1IH, [aH+2,...,a2H+1]T=IHAnd IHUnit is tieed up for H
Matrix,Represent robustness electric energy acquisition amount, i.e., the electricity obtained from battery within entire dispatching cycle is one
Robustness threshold value, [b2,...,bH+1]=[0 ..., 0]T, [bH+2,...,b2H+1]=ξmax·[1,1,...,1]TAnd
K=2H+1, λiIt is a decision variable；
DefinitionIt, can by interior point method for the fault tolerant probability under worst case
The optimal solution of the model is obtained, then satisfaction can be acquired by dichotomySolution, algorithm flow design it is as follows：
1) the Error Tolerance ε that input-mean μ, second moment S, search radius ρ, battery charging and discharging constrain；
2) initial ranging section [0, ρ] is defined；
3) the optimal solution K of subproblem is solved with interior point methodξ(b1)；
4) it is solved with dichotomyThenSolar power generation amount as under worst case is one and determines value, output
So far, as the subproblem of primal problem, which has been obtained the solar energy yield under worst case, and stochastic variable is become
Determining variable, the inequality constraints (9) in primal problem are converted into
10. the power generation dispatching technology of time correlation regenerative resource involved in micro-capacitance sensor according to claim 1, feature
It is：Primal problem becomes the mixed integer programming for not containing stochastic variable, by including branch and bound method, Surface by Tangent Plane Method
Method solve.
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| CN113901672A (en) * | 2021-11-17 | 2022-01-07 | 香港理工大学深圳研究院 | Optimal design method of wind-solar complementary power energy storage system for net zero energy consumption building application |
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| CN109685683A (en) * | 2018-12-11 | 2019-04-26 | 新奥数能科技有限公司 | For general energy scheduling model optimization method, apparatus, medium and the equipment that can be stood |
| CN113901672A (en) * | 2021-11-17 | 2022-01-07 | 香港理工大学深圳研究院 | Optimal design method of wind-solar complementary power energy storage system for net zero energy consumption building application |
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