CN104184172B - A kind of microgrid energy control method, GPU processors and system - Google Patents
A kind of microgrid energy control method, GPU processors and system Download PDFInfo
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- CN104184172B CN104184172B CN201410265163.2A CN201410265163A CN104184172B CN 104184172 B CN104184172 B CN 104184172B CN 201410265163 A CN201410265163 A CN 201410265163A CN 104184172 B CN104184172 B CN 104184172B
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Abstract
This application discloses a kind of microgrid energy control method, GPU processors and system, the method includes the energy hole object function for arranging micro-capacitance sensor, and obtains the optimal solution of control variables in the object function using population film algorithm.The population film algorithm is combined with film computational algorithm and particle cluster algorithm, multilayer underlying membrane and one layer of top layer film are obtained using film computational algorithm, and execute particle cluster algorithm on every layer of underlying membrane simultaneously, obtain the alternative optimal particle on each layer underlying membrane, during each alternative optimal particle described is exported to top layer film, so that it is determined that the optimal location value of the target optimal particle is defined as the optimal solution of control variables in energy hole object function by target optimal particle.Particle on each layer underlying membrane of the population film algorithm can execute particle cluster algorithm simultaneously, and convergence rate is very fast, and then obtain final goal optimal particle quickly, final realize the control efficiency higher to micro-capacitance sensor distributed power source generated output.
Description
Technical field
The application is related to power grid control technical field, especially a kind of microgrid energy control method, GPU processors and is
System.
Background technology
Micro-capacitance sensor is filled by distributed power source, energy storage device, energy conversion device, load, load monitoring and electricity protection
The small-sized electric system of composition such as put.Specifically, one or more distributed power source, such as light can be included in the micro-capacitance sensor
Volt TRT, wind power generation plant, hydroelectric installation, fuel cell and gas turbine etc., for producing needed for load
Electric energy.
The type of distributed power source is different, and the respective method of operation there is also difference.For example, the new forms of energy such as photovoltaic, wind-force
Generated output easily receive natural environment influence, but discharge pollutant less, conversely, the fossil energy such as fuel cell, gas turbine
The generated output in source is relatively stable, but can cause larger pollution to environment.Therefore, according to actual power demands and environment into
This requirement, needs to carry out different controls to the generated output of distributed power source in micro-capacitance sensor.
At present, a kind of control method of the generated output to the various distributed power sources there is no.
Content of the invention
In view of this, this application provides a kind of microgrid energy control method, GPU processors and system, in order to provide
A kind of control method to the generated output of various distributed power sources in micro-capacitance sensor.The technical scheme that the application is provided is as follows：
A kind of microgrid energy control method, including：
The energy hole object function of micro-capacitance sensor is set；Wherein, include control variables in the energy hole object function,
The control variables includes the generated output of various distributed power sources in the micro-capacitance sensor；
The execution parameter of the default population film algorithm of initialization；Wherein, include multilayer in the default population film algorithm
Underlying membrane and one layer of top layer film, and the execution parameter includes the corresponding iterations threshold of the number of plies of underlying membrane, the underlying membrane
Particle in value, the corresponding iterations threshold value of the top layer film, the top layer film current iteration number of times, the population film algorithm
Number, the position of each particle and speed；
According to the energy hole object function, the fitness function of the particle cluster algorithm is generated；
In there is the particle of position and speed to be randomly assigned to each layer underlying membrane described each, and according to described suitable
Response function and the corresponding iterations threshold value of the underlying membrane, are carried out to the speed and position of particle on underlying membrane per layer described
Update, to obtain the alternative optimal particle on per layer of underlying membrane；Wherein, include at least one institute in per layer of underlying membrane
State particle；
Each alternative optimal particle described is sent in the top layer film, and target optimum grain is determined in the top layer film
Son；Wherein, the target optimal particle has optimal location value；
Judge whether the top layer film current iteration number of times reaches the corresponding iterations threshold value of the top layer film；
If so, the optimal location value of the target optimal particle is defined as the optimum of the energy hole object function
Solution, so that obtain the generated output of the various distributed power sources；
Otherwise, the top layer film current iteration number of times is updated, and according to the target optimal particle, is returned to described in per layer
On underlying membrane, the speed of particle and position are updated, to regain the alternative optimal particle on per layer of underlying membrane.
Said method, alternatively, the energy hole object function for arranging micro-capacitance sensor includes：
Arrange micro-capacitance sensor energy hole object function be：
Wherein, T is period sum in micro-capacitance sensor controlling cycle；N is the kind number of the distributed power source；Fi[P (t)] is to divide
Fuel cost of the cloth power supply in period t；Oi[P (t)] is operating cost of the distributed power source in period t；M is distributed power source
The type sum for discharging pollutants；akFor distributed electrical source emission type for k pollutant emission factor；For distribution
The processing cost that formula power supply discharges pollutants in period t；Cb(t)Pbuy(t)-Cs(t)PsellT () is carried out with main electrical network for period t
Electricity transaction cost, when the value be on the occasion of when represent to main electrical network purchases strategies, when the value be negative value when represent to main electrical network
Sale of electricity income；CbT () is prices of the period t to main electrical network power purchase；Pbuy(t) be period t to main electrical network purchase of electricity；Cs(t) period t
Price to main electrical network sale of electricity；Psell(t) be period t to main electrical network electricity sales amount；CdepFor burning natural gas distributed power apparatus depreciation into
This.
Said method, alternatively, the distributed power source includes diesel-driven generator, fuel cell and gas turbine, then：
The fuel cost F [P (t)] of the diesel-driven generator utilizes F [P (t)]=aiP2+biP+ciObtain；Wherein, ai、bi、
ciFor default cost function coefficients；P is generated output of the diesel-driven generator in period t；
The fuel cost F [P (t)] of the fuel cost of the fuel cell and the gas turbine is availableObtain；Wherein, CnlFor consuming the price of fuel；LHV is the calorific value for consuming fuel；PJFor when
The power output of section t；ηJGross efficiency for period t fuel cells.
Said method, alternatively, described according to the fitness function and the corresponding iterations threshold value of the underlying membrane,
The speed and position of particle on underlying membrane per layer described are updated, to obtain the alternative optimum grain on per layer of underlying membrane
Son, including：
According to position and the fitness function of each particle, the adaptation of particle on per layer of underlying membrane is obtained
Angle value；
The Inertia Weight of per layer of underlying membrane is updated, and grain on per layer of underlying membrane is updated according to the Inertia Weight
The speed of son and position；
Judge whether the update times reach the corresponding iterations threshold value of the underlying membrane；
If so, according to the fitness value, the speed and the position, the alternative optimum grain on the underlying membrane is obtained
Son；
Otherwise, return and execute the Inertia Weight for updating per layer of underlying membrane, and per layer is updated according to the Inertia Weight
The speed of particle and position on the underlying membrane.
Said method, alternatively, is defined as the energy control in the optimal location value by the target optimal particle
The optimal solution of object function processed, after obtaining the generated output of the various distributed power sources, also includes：
According to the generated output of the various described distributed power source for obtaining, respectively to the various distributions in the micro-capacitance sensor
Formula power supply is controlled.
Present invention also provides a kind of GPU processors, including：
Object function arranging unit, for arranging the energy hole object function of micro-capacitance sensor；Wherein, the energy hole mesh
Include control variables in scalar functions, the control variables includes the generated output of various distributed power sources in the micro-capacitance sensor；
Parameter initialization unit is executed, for initializing the execution parameter of default population film algorithm；Wherein, described default
Comprising multilayer underlying membrane and one layer of top layer film in population film algorithm, and the execution parameter includes the number of plies of underlying membrane, described
The corresponding iterations threshold value of underlying membrane, the corresponding iterations threshold value of the top layer film, the top layer film current iteration number of times,
The number of particle, the position of each particle and speed in the population film algorithm；
Fitness function signal generating unit, for according to the energy hole object function, generating the particle cluster algorithm
Fitness function；
Alternative particle acquiring unit, for there is position and the particle of speed to be randomly assigned to each layer described each
In underlying membrane, and according to the fitness function and the corresponding iterations threshold value of the underlying membrane, to underlying membrane per layer described
The speed of upper particle and position are updated, to obtain the alternative optimal particle on per layer of underlying membrane；Wherein, described in per layer
Include particle described at least one in underlying membrane；
Intended particle acquiring unit, for each alternative optimal particle described is sent in the top layer film, described
Target optimal particle is determined in the film of top layer；Wherein, the target optimal particle has optimal location value；
End condition judging unit, corresponding for judging whether the top layer film current iteration number of times reaches the top layer film
Iterations threshold value；If so, Function Optimization solution determining unit is triggered, otherwise, alternative particle updating block is triggered；
Control variables determining unit, for being defined as the energy hole by the optimal location value of the target optimal particle
The optimal solution of object function, so that obtain the generated output of the various distributed power sources；
Alternative particle updating block, for according to the target optimal particle, returning to particle on underlying membrane per layer described
Speed and position be updated, to regain the alternative optimal particle on per layer of underlying membrane.
Present invention also provides a kind of microgrid energy control system, at CPU, parameter collection module, above-mentioned GPU
Reason device and micro-capacitance sensor, wherein：
The CPU, for initializing the speed of each particle and position in population film algorithm, and by each particle described
Send to the GPU；
The parameter collection module, for sending the operational factor of multiple distributed power sources in the micro-capacitance sensor of collection
To the GPU；
The GPU, for according to the operational factor, arranging the energy hole object function of the micro-capacitance sensor, and foundation
Each described initialized particle, executes default population film algorithm to obtain control variables in the energy hole object function
Optimal solution, and the optimal solution of the control variables is sent to the CPU；Wherein, the control variables is comprising described various
The generated output of distributed power source；
The CPU, the multiple distributed power sources in being additionally operable to the optimal solution according to the control variables to the micro-capacitance sensor
Generated output be controlled.
The technical scheme that the application is provided, with following beneficial effects：
The energy hole object function of micro-capacitance sensor, in a kind of microgrid energy control method that the application is provided, is set, and
The optimal solution that control variables in the object function is obtained using population film algorithm.The population film algorithm is combined with film calculating
Algorithm and particle cluster algorithm, obtain multilayer underlying membrane and one layer of top layer film using film computational algorithm, and same on every layer of underlying membrane
Shi Zhihang particle cluster algorithms, obtain the alternative optimal particle on each layer underlying membrane, by each alternative optimal particle described export to
In the film of top layer, so that it is determined that the optimal location value of the target optimal particle is defined as energy hole target by target optimal particle
The optimal solution of control variables in function.The population film algorithm is combined with film computational algorithm and particle cluster algorithm, due to film meter
Each layer underlying membrane in calculation has not interactive characteristic, the particle on each layer underlying membrane is calculated while executing population
Method, convergence rate are very fast, and then obtain final goal optimal particle quickly, and final realization generates electricity to micro-capacitance sensor distributed power source
The higher control efficiency of power.
Description of the drawings
For the technical scheme being illustrated more clearly that in the embodiment of the present application, below will be to making needed for embodiment description
Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present application, for
For those of ordinary skill in the art, on the premise of not paying creative work, can be obtaining other according to these accompanying drawings
Accompanying drawing.
A kind of flow chart of microgrid energy control method embodiment one that Fig. 1 is provided for the application；
A kind of partial process view of microgrid energy control method embodiment two that Fig. 2 is provided for the application；
A kind of structural representation of GPU processors that Fig. 3 is provided for the application；
A kind of parallel computation flow chart of GPU processors that Fig. 4 is provided for the application；
A kind of GPU-CUDA parallel programming models that Fig. 5 is provided for the application；
A kind of structural representation of microgrid energy control system that Fig. 6 is provided for the application；
A kind of topology diagram of microgrid energy control system that Fig. 7 is provided for the application；
Test day photovoltaic generation and the generated output schematic diagram of wind-power electricity generation that Fig. 8 is provided for the application；
The test day power load power demand schematic diagram that Fig. 9 is provided for the application；
The experimental result picture based on adaptive particle swarm optimization algorithm that Figure 10 is provided for the application；
The experimental result picture based on TSP question particle swarm optimization algorithm that Figure 11 is provided for the application；
The experimental result picture based on blooming adaptive particle swarm optimization algorithm that Figure 12 is provided for the application；
The experimental result picture of the population film algorithm provided based on the application that Figure 13 is provided for the application.
Specific embodiment
Accompanying drawing in below in conjunction with the embodiment of the present application, to the embodiment of the present application in technical scheme carry out clear, complete
Site preparation is described, it is clear that described embodiment is only some embodiments of the present application, rather than whole embodiments.It is based on
Embodiment in the application, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of the application protection.
Referring to Fig. 1, the flow chart that it illustrates a kind of microgrid energy control method embodiment one of the application offer should
Embodiment is specifically included：
Step 101：The energy hole object function of micro-capacitance sensor is set；Wherein, include in the energy hole object function
Control variables, the control variables include the generated output of various distributed power sources in the micro-capacitance sensor.
Wherein, the energy hole object function of micro-capacitance sensor, according to micro-capacitance sensor actual motion demand, is set.For example, micro-capacitance sensor
When it is required that fuel cost is minimum, the function for consuming fuel cost with regard to distributed power source can be set；Micro-capacitance sensor requires environment dimension
When shield cost is minimum, the function with regard to processing the cost that distributed power source discharges pollutants can be set；Micro-capacitance sensor requires comprehensive
When cost is minimum, can arrange and discharge pollutants with regard to distributed power source consumption fuel cost, equipment operation maintenance cost, process
Cost and the various cost factors such as equipment depreciation cost in interior function.
It should be noted that the variable included in the object function is regarded as control variables, that is, control micro- electricity
Net the variable of various distributed power source generated outputs.The energy hole object function shows that various distributed power sources are needed
Want under each what kind of generated output of leisure, could realize optimum operation demand, such as fuel cost is minimum, environment maintenance cost most
Low or integrated cost is most low.
Step 102：The execution parameter of the default population film algorithm of initialization；Wherein, in the default population film algorithm
Comprising multilayer underlying membrane and one layer of top layer film, and the execution parameter includes that the number of plies of underlying membrane, the underlying membrane are corresponding repeatedly
Calculate for frequency threshold value, the corresponding iterations threshold value of the top layer film, the top layer film current iteration number of times, the population film
The number of particle, the position of each particle and speed in method.
Wherein, the concrete implementation procedure of the default population film algorithm comprises the steps 103 to step 108.Need
Illustrate, the population film algorithm is combined by particle cluster algorithm and film computational algorithm, in the film computational algorithm
Include multilayer underlying membrane and one layer of top layer film, then need the number of plies for initializing the underlying membrane.
Meanwhile, need particle cluster algorithm to be executed on per layer of underlying membrane, then need to initialize in particle cluster algorithm
The number of times that particle updates in particle number, the velocity amplitude of each particle and positional value and the particle cluster algorithm is underlying membrane pair
The iterations threshold value that answers.Meanwhile, population film algorithm needs for the optimal particle selected on the film of top layer to be back to each layer
On underlying membrane, accordingly, it would be desirable to the corresponding iterations threshold value of initial table tunic, is used to determine whether to continue described return
Journey.
It should be noted that the velocity amplitude and positional value of initialization particle, wherein, institute's location value shows in object function
The alternative solution of control variables, the velocity amplitude show changing value of the alternative solution of control variables during an iteration.
Step 103：According to the energy hole object function, the fitness function of the particle cluster algorithm is generated.
Wherein, need the fitness value that each particle is calculated according to fitness function in the particle cluster algorithm, then need
Define the fitness function of the particle cluster algorithm.Optionally, using step 101 China arrange energy hole object function as
Fitness function, when the population film algorithm is executed, takes the particle for making the fitness function be minimum of a value as target
Optimal particle.That is, the target optimal particle also makes energy Controlling object function obtain minimum of a value, so as to reach the energy
The required energy hole that realizes of Controlling object function is required.
Step 104：In there is the particle of position and speed to be randomly assigned to each layer underlying membrane described each, and according to
According to the fitness function and the corresponding iterations threshold value of the underlying membrane, to the speed of particle on underlying membrane per layer described and
Position is updated, to obtain the alternative optimal particle on per layer of underlying membrane；Wherein, comprising extremely in per layer of underlying membrane
A few particle.
Wherein, each particle is randomly assigned, needs to ensure in every layer of underlying membrane, all to have included at least a particle.To each layer
Particle on underlying membrane executes particle cluster algorithm simultaneously, then can obtain optimal particle on every layer of underlying membrane, and the optimal particle is
Alternative optimal particle.
Specifically, the positional value of each particle is substituted into the fitness function and can obtains the respective adaptation of each particle
Angle value, by judging the size of each fitness value, it may be determined that each self-corresponding individual optimal value of each particle, and
Determine colony optimal value in the plurality of particle, according to the individual optimal value and colony's optimal value to this layer of underlying membrane in each
The speed of individual particle and positional value are updated, until the update times meet the corresponding iterations of the underlying membrane, will
The preferred particle of final choice is defined as the alternative optimal particle on this layer of underlying membrane.
Step 105：Each alternative optimal particle described is sent in the top layer film, and mesh is determined in the top layer film
Mark optimal particle；Wherein, the target optimal particle has optimal location value.
Wherein, the alternative optimal particle on each layer underlying membrane is sent in the top layer film, is had in the top layer film
With the alternative optimal particle of the underlying membrane number of plies equivalent, and then, in the plurality of alternative optimal particle choose optimal particle,
Individual optimal value of the individual optimal value of the optimal particle that chooses with the optimal particle of selection in last iterative process is entered
Row compares, if meeting default more new standard, this optimal particle for selecting is defined as target optimal particle.Need explanation
It is to include positional value in the individual optimal value of the target optimal particle.
Step 106：Judge whether the top layer film current iteration number of times reaches the corresponding iterations threshold of the top layer film
Value；If so, execution step 107；Otherwise, execution step 108.
Wherein, during initialization, the top layer film current iteration number of times is initialized as 1, top layer film is determined each time
Target optimal particle when being back to each layer underlying membrane, update the top layer film current iteration number of times, i.e., to the top layer
Film current iteration number of times adds 1.Meanwhile, need to judge whether the top layer film current iteration number of times meets the initialized top layer
The corresponding iterations threshold value of film.Wherein, the current iteration frequency threshold value is used as the control population film algorithm performs end
Condition only.
Step 107：The optimal location value of the target optimal particle is defined as the energy hole object function most
Excellent solution, so that obtain the generated output of the various distributed power sources.
Wherein, the optimal location value of the target optimal particle is defined as the energy hole target that arrange in step 101
The optimal solution of control variables in function.As the control variables shows the generated output of the various distributed power sources, therefore,
The optimal solution can be defined as the optimal power generation power of various distributed power sources.
Step 108：The top layer film current iteration number of times is updated, and according to the target optimal particle, is returned to per layer
On the underlying membrane, the speed of particle and position are updated, to regain the alternative optimum grain on per layer of underlying membrane
Son.
Wherein, the top layer film current iteration number of times is updated, is that the top layer film current iteration number of times is added 1.By institute
State target optimal particle to be back on per layer of underlying membrane, to substitute the alternative optimal particle on each layer underlying membrane and then right
On each layer underlying membrane, the velocity amplitude and positional value of particle is updated, so as to realize using the target selected from each layer underlying membrane
Optimal particle is affecting the renewal of particle on each layer underlying membrane.
In a kind of microgrid energy control method provided from above technical scheme, the application, micro-capacitance sensor is set
Energy hole object function, and obtain the optimal solution of control variables in the object function using population film algorithm.The grain
Subgroup film algorithm is combined with film computational algorithm and particle cluster algorithm, obtains multilayer underlying membrane and one layer of top layer using film computational algorithm
Film, and particle cluster algorithm is executed simultaneously on every layer of underlying membrane, the alternative optimal particle on each layer underlying membrane is obtained, will be described each
During individual alternative optimal particle is exported to top layer film, so that it is determined that target optimal particle, by the optimal location of the target optimal particle
Value is defined as the optimal solution of control variables in energy hole object function.
It should be noted that this algorithm is combined with film computational algorithm and particle cluster algorithm, each layer base in being calculated due to film
This film has not interactive characteristic, makes the particle on each layer underlying membrane can be while execute particle cluster algorithm, convergence rate
Comparatively fast, and then final goal optimal particle is obtained quickly, final realization is higher to micro-capacitance sensor distributed power source generated output
Control efficiency.
Meanwhile, for being defined as colony's optimal particle compared to the local optimum particle for obtaining particle cluster algorithm, this Shen
In the control method that please be provide, using the population film algorithm for combining particle cluster algorithm with film computational algorithm, by generating
Each particle be distributed on each layer underlying membrane, on every layer of underlying membrane obtain local optimum particle, and utilize every layer of underlying membrane
On local optimum particle each particle on each layer underlying membrane is updated, so as to avoid the optimal value of target optimal particle
Premature Convergence reduces the error of target optimal particle optimal value, so as to improve the accuracy of control method in local.
It should be noted that in said method embodiment arrange micro-capacitance sensor energy hole object function can pass through following
Mode is realized：
Arrange micro-capacitance sensor energy hole object function be：
Wherein, T is period sum in micro-capacitance sensor controlling cycle；N is the kind number of the distributed power source；Fi[P (t)] is to divide
Fuel cost of the cloth power supply in period t；Oi[P (t)] is operating cost of the distributed power source in period t；M is distributed power source
The type sum for discharging pollutants；akFor distributed electrical source emission type for k pollutant emission factor；For distribution
The processing cost that formula power supply discharges pollutants in period t；Cb(t)Pbuy(t)-Cs(t)PsellT () is carried out with main electrical network for period t
Electricity transaction cost, when the value be on the occasion of when represent to main electrical network purchases strategies, when the value be negative value when represent to main electrical network
Sale of electricity income；CbT () is prices of the period t to main electrical network power purchase；Pbuy(t) be period t to main electrical network purchase of electricity；Cs(t) period t
Price to main electrical network sale of electricity；Psell(t) be period t to main electrical network electricity sales amount；CdepFor burning natural gas distributed power apparatus depreciation into
This.
Optionally, the distributed power source for including in micro-capacitance sensor can be photovoltaic power generation apparatus (Photovoltaic, PV), wind
Power generation device (Wind Turbine, WT), diesel-driven generator (Diesel Engine, DE), fuel cell (Fuel Cell,
) and gas turbine (Micro Turbine, MT) FC.As photovoltaic generation and wind-power electricity generation depend on extraneous natural environment, which is defeated
The generated output for going out is random, fluctuation and with gap.But meanwhile, without the need for profit during photovoltaic generation and wind-power electricity generation
With fossil fuel, environmental pollution is less.Therefore, it can the generated output for predicting photovoltaic generation and wind-power electricity generation in advance, so as to
The generated output of described two TRTs is set to predicted value, and is solved using above-mentioned energy hole object function described surplus
Excess-three kind is each self-corresponding generated output of diesel-driven generator, fuel cell and gas turbine.Therefore, in above-mentioned object function
N could be arranged to 3, i.e. diesel-driven generator, fuel cell and gas turbine.
It should be noted that above-mentioned Cb(t)Pbuy(t)-Cs(t)PsellT () represents purchases strategies of the micro-capacitance sensor to main electrical network
Or sale of electricity income.Wherein, when needing to main electrical network power purchase, the Cs(t)PsellT () is 0, when needs are when main electrical network sale of electricity,
The Cb(t)PbuyT () is 0.Certainly, the generated output of above-mentioned various distributed power sources needs to meet following constraintss：
Wherein, PLFor micro-capacitance sensor internal loading power demand；For diesel-driven generator, fuel cell and gas turbine three
Kind of distributed power source generated output and value；PPVGenerated output for photovoltaic generation；PWTGenerated output for wind-power electricity generation；
Pbuy-PsellIt is micro-capacitance sensor to main electrical network power purchase or electricity sales amount.
Meanwhile, in addition to the constraints of formula (2), the control variables in energy hole object function also needs to meet following
Constraint equation (3) and constraint equation (4).
Wherein,For the minimum generated output of distributed power source,Maximum generation work(for distributed power source
Rate.For example, each self-corresponding minimum generated output of gas turbine, fuel cell and diesel-driven generator is respectively 5,4 and 0, each
Corresponding maximum power generation is respectively 65,10 and 30.
Formula (3) is represented：Electricity transaction P between micro-capacitance sensor and main electrical networkline(Pbuy-Psell) need in power transmission medium
Transmission limit value within.
From such scheme, energy hole object function minC (P) that the present embodiment determines shows various distributed electricals
, respectively under the conditions of which kind of value, the integrated cost C (P) of micro-capacitance sensor is minimum for the generated output in source.Wherein, in the integrated cost
Include distributed power source to consume the cost of fuel, the operating cost of distributed power source, process distributed power source and discharge pollutants
Cost and burning natural gas distributed power apparatus depreciable cost.Certainly, if Cb(t)Pbuy(t)-Cs(t)Psell(t) be on the occasion of when, described
Also include micro-capacitance sensor in integrated cost to the purchases strategies of main electrical network.The energy hole object function requirement is accomplished that micro-
The integrated cost of electrical network is minimum, has simultaneously taken account of the generated energy of the distributed power source, consumption of fuel, the impact to environment, right
The maintenance of equipment and with the power trade between main electrical network, realize micro-capacitance sensor is more comprehensively controlled.
Alternatively, the O that energy hole object function (1) includes in said method embodimenti[P (t)] can utilize following formula
Obtain：
Oi[P (t)]=Kom(i)×P(t)；
Wherein, Kom(i)The respective operating cost coefficient of operating cost coefficient, such as fuel cell, gas turbine and diesel-driven generator
Respectively 0.029 yuan/KWh, 0.0356 yuan/KWh and 0.088 yuan/KWh.
The C that energy hole object function (1) includes in above-described embodimentdepCan be obtained using following formula：
Wherein,Buying and mounting cost for burning natural gas distributed power apparatus；Recovery system for burning natural gas distributed power apparatus
Number；Available hours number for burning natural gas distributed power apparatus.
It should be noted that in said method embodiment, need in advance to photovoltaic power generation apparatus and wind power generation plant
Generated output is predicted.
Alternatively, for the prediction of photovoltaic power generation apparatus generated output, can be accomplished by：
Wherein, PstcMaximum survey under for standard test condition (sunshine incident intensity 1000W/m2,25 DEG C of environment temperature)
Examination power such as 2KW；GacFor intensity of illumination；GstcFor the such as 1000W/m of intensity of illumination under standard test condition2；K is power temperature system
Number；TcFor photovoltaic battery panel operating temperature；TτFor such as 25 DEG C of preset reference temperature.It should be noted that the operating temperature of cell panel
TcMore difficult acquisition, can estimate acquisition by test environment temperature.Specifically：
Wherein, TcFor battery panel components temperature；TamdFor the environment temperature that measurement is obtained；G is received for battery panel components
Solar radiation value.
Alternatively, for the prediction of wind power generation plant generated output, can be accomplished by：
Wherein, VciFor cutting wind speed；VcoFor cutting off wind speed；VrFor rated wind speed；PrFor different wind-driven generator models Vco
Rated power.
Optionally, referring to Fig. 2, according to the fitness function and described basic in step 104 in said method embodiment
The corresponding iterations threshold value of film, is updated to the speed and position of particle on underlying membrane per layer described, to obtain per layer of institute
The implementation for stating the alternative optimal particle on underlying membrane may comprise steps of：
Step 201：According to position and the fitness function of each particle, grain on per layer of underlying membrane is obtained
The fitness value of son.
Wherein, the positional value of each particle is substituted into the fitness function, so as to obtain each particle
Fitness value.
Step 202：The Inertia Weight of per layer of underlying membrane is updated, and per layer of base is updated according to the Inertia Weight
The speed of particle and position on this film.
Wherein it is possible to utilizeUpdate the inertia power of underlying membrane
Value.Specifically, ω is Inertia Weight, wmaxFor default particle maximum Inertia Weight, wminFor default particle minimum inertia power
Value, k is the current iteration number of times of every layer of underlying membrane, kmaxIt is the iterations threshold value of every layer of underlying membrane.Need explanation
It is the Inertia Weight of underlying membrane to be updated using above-mentioned formula, when the energy hole object function is non-linear complexity
During function, it is possible to achieve the optimization to the energy hole object function.Specifically, due to adopting in the formula of renewal Inertia Weight
With exp functions so that the value of Inertia Weight change is no longer evenly distributed, and with more diversity, improves the receipts of algorithm
Hold back speed.Also, in the Changing Pattern of Inertia Weight, initial Inertia Weight can quickly determine optimal solution than larger
Approximate location, when iterations increases, Inertia Weight is gradually reduced, and can so improve the search precision of algorithm.
Wherein, in every layer of underlying membrane, each particle has its respective velocity amplitude and positional value.According to the inertia
Each particle rapidity value and each particle position value of every layer of underlying membrane described in right value update.Specifically, utilizeEach particle rapidity value of every layer of underlying membrane is calculated, is utilizedCalculate each particle position value of every layer of underlying membrane.
Wherein, w is the Inertia Weight of every layer of underlying membrane,For t for when colony optimal value,For t generations
When i-th particle individual optimal value,For i-th particle t for when positional value,It is i-th particle in t generations
When velocity amplitude, in interval [- vdmax,vdmax] value,For i-th particle t+1 for when positional value,For i-th
Individual particle t+1 for when velocity amplitude.
Step 203：Judge whether the update times reach the corresponding iterations threshold value of the underlying membrane；If so, hold
Row step 204, otherwise, returns execution step 202.
Wherein, the update times refer to the iterations carried out on the underlying membrane.
Step 204：According to the fitness value, the speed and the position, obtain on the underlying membrane alternative most
Excellent particle.
Wherein, its velocity amplitude and positional value is chosen in every layer of underlying membrane meet default with the fitness value
Particle with rule, and then the alternative optimal particle on the underlying membrane is determined in the particle of the selection.
From above technical scheme, during the particle cluster algorithm executed on every layer of underlying membrane in the present embodiment, constantly more
The Inertia Weight of new underlying membrane.As inertia weight ω plays a part of to weigh local optimum ability and global optimum's ability, compared with
Big ω is conducive to jumping out local minimum, is easy to global search, and less ω is conducive to accurately searching for current search region
Rope, is easy to algorithmic statement.In the technical scheme that the application is provided, Inertia Weight constantly declines with the increase of update times, from
And algorithm just can determine the approximate location of optimal solution quickly in the beginning search phase, and the flying speed of particle is slack-off, is beneficial to
Optimal solution is searched exactly in subrange.
Optionally, on the basis of said method embodiment, can also include：
Step 109：According to the generated output of the various described distributed power source for obtaining, respectively to various in the micro-capacitance sensor
The distributed power source is controlled.
Wherein, the generated output of various distributed power sources is adjusted so as to meet and solve the target optimum grain for obtaining
The optimal value of son, so as to realize the optimal solution of energy hole object function, the integrated cost of such as micro-capacitance sensor is minimum.
It should be noted that step 101 arranges the energy hole object function of the micro-capacitance sensor in said method embodiment
Including：
Obtain that each default control is quantitative and control variables, will the life of quantitative and described for each default control described control variables
Energy hole object function into the micro-capacitance sensor.
Wherein, each known quantity in the quantitative namely energy hole object function of the default control, such as micro-capacitance sensor fortune
The demand power and the fuel consumption cost of distributed power source of load in operational management coefficient, micro-capacitance sensor in row cost function
Deng.Specifically,
Distributed power source be respectively FC, DE and MT when, respective operational management coefficient be respectively 0.0293,0.088 and
0.0359.
In micro-capacitance sensor, the demand power of load is as shown in table 1：
Table 1
The prediction generated output of photovoltaic power generation apparatus is as shown in table 2：
Table 2
The prediction generated output of wind power generation plant is as shown in table 3：
Table 3
The processing cost and emission factor that distributed power source discharges pollutants is as shown in table 4：
Table 4
The depreciable cost parameter of burning natural gas distributed power apparatus is as shown in table 5：
Table 5
Referring to Fig. 3, a kind of structural representation of GPU processors of the application offer, the process implement body bag is it illustrates
Include：Object function arranging unit 301, execution parameter initialization unit 302, fitness function signal generating unit 303, alternative particle are obtained
Take unit 304, intended particle acquiring unit 305, end condition judging unit 306, control variables determining unit 307 and alternative grain
Sub- updating block 308.Wherein,
The object function arranging unit 301, for arranging the energy hole object function of micro-capacitance sensor；Wherein, the energy
Include control variables in amount Controlling object function, the control variables includes the generating of various distributed power sources in the micro-capacitance sensor
Power；
The execution parameter initialization unit 302, for initializing the execution parameter of default population film algorithm；Wherein,
Comprising multilayer underlying membrane and one layer of top layer film in the default population film algorithm, and the parameter that executes includes the layer of underlying membrane
The corresponding iterations threshold value of the several, underlying membrane, the corresponding iterations threshold value of the top layer film, the top layer film currently change
The number of particle, the position of each particle and speed in generation number, the population film algorithm；
The fitness function signal generating unit 303, for according to the energy hole object function, generating the population
The fitness function of algorithm；
The alternative particle acquiring unit 304, for by described each have the particle of position and speed be randomly assigned to
In each layer underlying membrane, and according to the fitness function and the corresponding iterations threshold value of the underlying membrane, to per layer of institute
State the speed of particle and position on underlying membrane to be updated, to obtain the alternative optimal particle on per layer of underlying membrane；Wherein,
Include particle described at least one in per layer of underlying membrane；
The intended particle acquiring unit 305, for each alternative optimal particle described is sent in the top layer film,
Target optimal particle is determined in the top layer film；Wherein, the target optimal particle has optimal location value；
The end condition judging unit 306, for judging whether the top layer film current iteration number of times reaches the table
The corresponding iterations threshold value of tunic；If so, the Function Optimization solution determining unit 307 is triggered, otherwise, the alternative grain is triggered
Sub- updating block 308；
The control variables determining unit 307, described for the optimal location value of the target optimal particle to be defined as
The optimal solution of energy hole object function, so that obtain the generated output of the various distributed power sources；
The alternative particle updating block 308, for according to the target optimal particle, returning to underlying membrane per layer described
The speed of upper particle and position are updated, to regain the alternative optimal particle on per layer of underlying membrane.
Existing control process device is the processor such as DSP for executing single task, or the system calculated by software,
The concurrent operation of high speed cannot be realized, the operational efficiency of existing control process device is relatively low.The GPU processors that the application is provided
Film computational algorithm and particle cluster algorithm is combined with processing procedure, and each layer underlying membrane in calculating due to film has not mutual
The characteristic of impact, makes the particle on each layer underlying membrane can be while execute particle cluster algorithm, and convergence rate is very fast, and then obtains quickly
Obtain final goal optimal particle, the final realization control efficiency higher to micro-capacitance sensor distributed power source generated output.
It should be noted that the GPU processors (graphic process unit) that above-described embodiment is provided, as therein has a lot
The transistor for data processing, it is adaptable to high concurrency, data-intensive and predictable computation schema, it is possible in time
The scheduling of resource and pattern switching of schedule system.Meanwhile, which can be used as a coprocessor, produce substantial amounts of thread, by big
The parallel computation of amount thread is eliminating the delay that memory is caused because of access.
Alternatively, GPU realizes that the mode of parallel computation is as shown in Figure 4.CUDA(Compute Unified Device
Architecture) under parallel computation framework, thread (Thread) be display chip execute calculate when least unit, each line
Journey has a distinctive register, multiple threads to may make up a thread block (Block), in actual running, line
Journey block can be divided into less thread beam, and in thread block, whole threads can carry out access visit to the shared drive in block.
Whole thread block in multiprocessor constitutes one and calculates lattice (Grid).The instruction repertorie that each thread is executed in GPU is constituted
Preset function, preset function internal thread independent parallel are executed, and serial between different preset functions is executed.
Further, referring to Fig. 5, a kind of GPU-CUDA parallel programming models it illustrates.Wherein, CUDA employs list
Instruction multithreading executes model, and the model can divide and be summarized as six steps：
Step 1：Data type is initialized.Wherein, including the definition and statement of variable.
Step 2：Memory allocation.Wherein, including being that CPU ends and GPU ends variable are respectively allocated memory space, for depositing
Store up different types of data.
Step 3：Data transfer.Specially：GPU ends are transferred data to from host side.
Step 4：Executed in parallel.Specially：Preset function distribution GPU ends are called to execute parameter, the preset function will be by
The all threads being assigned in GPU are respectively executed 1 time, so as to realize the parallel process of single-instruction multiple-data.
Step 5：As a result return.Specially：The result that GPU ends calculate is delivered to host CPU end.
Step 6：Release video memory.Specially：Space is reclaimed in the global storage at GPU ends.
It should be noted that GPU is during algorithm performs, as follows the step of memory allocation：
Step 1：The An that N number of one-dimension array of population at individual is individually identified as A1, A2 ..., then will be according to identifier from little
End to end composition one-dimensional data group B of big order is arrived, the memory space shared by one-dimensional data group B is designated C.Its
In, include M element in each one-dimension array.
Step 2：Calculate memory space C value, and pass through CPU and GPU ends call respectively malloc () function and
CudaMalloc () function, storage space of the allocated size for C on the global storage of GPU.
Step 3：It is parameter, variable distribution memory space D in global register, then data B on internal memory is copied
Arrive global register D.
Step 4：Data in D are once cut per M element, is obtained N group data corresponding with initial data, point
The Dn that is not designated D1, D2 ....
Step 5：In host side by calling cudaMemcpy () function by the data information transfer of particle to GPU video memorys
On.Due to, GPU tissue include thread clathrum, thread block layer and the thread grid of thread layer, i.e., include two or two with
Upper thread block, each thread block include two or more threads, while each thread block has in the block thread block
The visible shared memory of all threads.Further, set and complete the thread grid of all computings and have N number of thread block, identify respectively
For E1, E2 ... En, data D1, D2 ... Dn is correspondingly imported the shared memory of E1, E2 ... En respectively.
Step 6：Inside GPU each thread block, the shared memory for belonging to the thread block is carried out fitness to particle
Value assessment, is as a result equally stored in the shared memory of the thread block.N number of thread block executes same operation, synchronous each
All threads in thread block, then export to D1, D2 by corresponding for the operation result of shared memory in each thread block ...
Dn.By end to end for the result of D1, D2 ... Dn composition one-dimension array F, then pass through cudaMemcpy () function by the data of F
Copy internal memory G to
Step 7：By one-dimension array G, each element is once split, the Gn that is divided into G1, G2 ....
Step 8：GPU ends by cudaMemcpy () function by the optimal particle information copy for searching in host side
In depositing, algorithm search terminates.
Referring to Fig. 6, this application provides a kind of structural representation of microgrid energy control system, the system is specifically wrapped
Include：CPU401, parameter collection module 402, above-mentioned GPU processors 403 and micro-capacitance sensor 404, wherein：The CPU401 respectively with
The GPU processors 403 and the micro-capacitance sensor 404 are connected, the parameter collection module 402 respectively with the GPU403 and described
Micro-capacitance sensor 404 is connected.Wherein：
The CPU401, for initializing the speed of each particle and position in population film algorithm, and by described each
Particle is sent to the GPU processors.
The parameter collection module 402, for will collection the micro-capacitance sensor in multiple distributed power sources operational factor
Send to the GPU processors.
Wherein, the GPU processors 403 send collection signal to the parameter collection module 402, trigger the parameter and adopt
The control parameter of collection 402 pairs of micro-capacitance sensors of module is acquired, and the control parameter of the collection is back to the GPU processors
403.In the micro-capacitance sensor 404 of the collection of the parameter collection module 402, the control parameter of distributed power source can include generating
The various controls of energy hole object function are quantitative.It should be noted that the parameter collection module can be A/D converter.
The GPU processors 403, for according to the operational factor, arranging the energy hole target letter of the micro-capacitance sensor
Number, and according to initialized particle each described, execute default population film algorithm to obtain the energy hole object function
The optimal solution of middle control variables, and the optimal solution of the control variables is sent to the CPU；Wherein, the control variables bag
Generated output containing the various distributed power sources.
The CPU401, the multiple distributed electricals in being additionally operable to the optimal solution according to the control variables to the micro-capacitance sensor
The generated output in source is controlled.
Referring to Fig. 7, it illustrates this application provides a kind of topology diagram of microgrid energy control system.Wherein,
Distributed power source includes 65kW miniature gas turbines (MT), 2kW photovoltaic cells (PV), 10kW fuel cells (FC), 10kW wind-force
Generator (WT) and 30KW diesel-driven generators (DE).In addition, L1 is important load, L2 is general load, and L3 is insignificant load.
Based on the microgrid energy control method that the application is provided, inventor carries out following emulation experiment：
Arranging the distributed power source included in micro-capacitance sensor includes photovoltaic power generation apparatus, electric power generator, fuel cell, combustion
TRT in gas-turbine, diesel-driven generator and main electrical network.The population scale arranged in population film algorithm is particle number
For 30, the corresponding iterations threshold value of underlying membrane is 10, and film corresponding iterations threshold value in top layer is 50, in population film algorithm
Studying factors c1, c2 take 1,2 respectively.Meanwhile, the algorithm cycle for arranging population film is 1 day, when whole day is divided into 24
Section, 1 hour used as a calculation interval.Purchase electricity price and sale of electricity electricity price take 0.83 yuan in electricity consumption peak value, purchase electricity price and
Sale of electricity electricity price takes 0.49 yuan when at ordinary times, and purchase electricity price and sale of electricity electricity price take 0.17 yuan in low power consumption.Choose winter
As experimental day during typical day 9, the generated output of the photovoltaic power generation apparatus and wind power generation plant on the same day as shown in figure 8, wherein,
Broken line 1 represents that the generated output (concrete data are referring to table 3) of wind-power electricity generation, broken line 2 represent that the generated output of photovoltaic generation is (concrete
Data referring to table 2).The load in teaching building is chosen as power load, the power load power demand is (concrete as shown in Figure 9
Data referring to table 1).It should be noted that function of the microgrid energy Controlling object function for determining for formula (1).
Wherein, the generated output for solving the fuel cell for obtaining using the energy control method that the application is provided is 10kw,
The generated output of gas turbine is 5kw, and the generated output of diesel-driven generator is 0kw, and accordingly, the integrated cost of acquisition is
66.3514 yuan.
Meanwhile, according to above-mentioned pre-conditioned, it is utilized respectively four kinds of different algorithms and obtains microgrid energy control targe letter
The optimal solution of control variables in number, particle individuality adaptive optimal control angle value (integrated cost in object function) of generation and iteration time
Several relations is respectively as shown in Figure 10, Figure 11, Figure 12 and Figure 13.Wherein, the corresponding adaptive particle swarm optimization algorithms of Figure 10；Figure 11
Corresponding TSP question particle swarm optimization algorithm；The corresponding blooming adaptive particle swarm optimization algorithms of Figure 12；The corresponding the application of Figure 13
The population film algorithm of offer.
By contrast Figure 10 to Figure 13 fitness value, the application provide energy control method obtain synthesis into
This is minimum, and convergence of algorithm is fastest.Therefore, the control accuracy of the energy control method that the application is provided is higher, and
Control efficiency is higher.
It should be noted that each embodiment in this specification is described by the way of going forward one by one, each embodiment weight
Point explanation is all difference with other embodiment, between each embodiment identical similar part mutually referring to.
The foregoing description of the disclosed embodiments, enables professional and technical personnel in the field to realize or using the present invention.
Multiple modifications of these embodiments will be apparent for those skilled in the art, as defined herein
General Principle can be realized without departing from the spirit or scope of the present invention in other embodiments.Therefore, the present invention
The embodiments shown herein is not intended to be limited to, and is to fit to and principles disclosed herein and features of novelty phase one
The most wide scope for causing.
Claims (7)
1. a kind of microgrid energy control method, it is characterised in that include：
The energy hole object function of micro-capacitance sensor is set；Wherein, include control variables in the energy hole object function, described
Control variables includes the generated output of various distributed power sources in the micro-capacitance sensor；
The execution parameter of the default population film algorithm of initialization；Wherein, basic comprising multilayer in the default population film algorithm
Film and one layer of top layer film, and the execution parameter includes the corresponding iterations threshold value of the number of plies of underlying membrane, the underlying membrane, institute
State particle in the corresponding iterations threshold value of top layer film, the top layer film current iteration number of times, the population film algorithm
Number, the position of each particle and speed；
According to the energy hole object function, the fitness function of the population film algorithm is generated；
In there is the particle of position and speed to be randomly assigned to each layer underlying membrane each, and according to the fitness function
And the corresponding iterations threshold value of the underlying membrane, the speed and position of particle on underlying membrane per layer described are updated, with
Obtain the alternative optimal particle on per layer of underlying membrane；Wherein, include particle described at least one in per layer of underlying membrane；
Each alternative optimal particle described is sent in the top layer film, and target optimal particle is determined in the top layer film；
Wherein, the target optimal particle has optimal location value；
Judge whether the top layer film current iteration number of times reaches the corresponding iterations threshold value of the top layer film；
If so, the optimal location value of the target optimal particle is defined as the optimal solution of the energy hole object function, from
And obtain the generated output of the various distributed power sources；
Otherwise, the top layer film current iteration number of times is updated, and according to the target optimal particle, is returned to basic per layer described
On film, the speed of particle and position are updated, to regain the alternative optimal particle on per layer of underlying membrane.
2. method according to claim 1, it is characterised in that the energy hole object function bag of the setting micro-capacitance sensor
Include：
Arrange micro-capacitance sensor energy hole object function be：
Wherein, T is period sum in micro-capacitance sensor controlling cycle；N is the kind number of the distributed power source；Fi[P (t)] is distributed
Fuel cost of the power supply in period t；Oi[P (t)] is operating cost of the distributed power source in period t；M is distributed electrical source emission
The type sum of pollutant；akFor distributed electrical source emission type for k pollutant emission factor；For distributed electrical
The processing cost that source discharges pollutants in period t；Cb(t)Pbuy(t)-Cs(t)PsellT () carries out electricity for period t with main electrical network
The cost of transaction, when the value be on the occasion of when represent to main electrical network purchases strategies, when the value be negative value when represent to main electrical network sale of electricity
Income；CbT () is prices of the period t to main electrical network power purchase；Pbuy(t) be period t to main electrical network purchase of electricity；Cs(t) be period t to
The price of main electrical network sale of electricity；Psell(t) be period t to main electrical network electricity sales amount；CdepDepreciable cost for burning natural gas distributed power apparatus.
3. method according to claim 2, it is characterised in that the distributed power source includes diesel-driven generator, fuel electricity
Pond and gas turbine, then：
The fuel cost F [P (t)] of the diesel-driven generator is utilized
F [P (t)]=aiP2+biP+ciObtain；Wherein, ai、bi、ciFor default cost function coefficients；P be diesel-driven generator in the period
The generated output of t；
The fuel cost F [P (t)] of the fuel cost of the fuel cell and the gas turbine is available
Obtain；Wherein, CnlFor consuming the price of fuel；LHV is the calorific value for consuming fuel；PJPower output for period t；ηJFor the period
The gross efficiency of t fuel cells.
4. method according to claim 1, it is characterised in that described according to fitness function and the corresponding iteration of underlying membrane
Frequency threshold value, is updated to the speed and position of particle on underlying membrane per layer described, to obtain on per layer of underlying membrane
Alternative optimal particle, including：
According to position and the fitness function of each particle, the fitness of particle on per layer of underlying membrane is obtained
Value；
The Inertia Weight of per layer of underlying membrane is updated, and particle on per layer of underlying membrane is updated according to the Inertia Weight
Speed and position；
Judge whether update times reach the corresponding iterations threshold value of the underlying membrane；
If so, according to the fitness value, the speed and the position, the alternative optimal particle on the underlying membrane is obtained；
Otherwise, return and execute the Inertia Weight for updating per layer of underlying membrane, and according to described in per layer of Inertia Weight renewal
The speed of particle and position on underlying membrane.
5. method according to claim 1, it is characterised in that in the optimal location value by the target optimal particle
It is defined as the optimal solution of the energy hole object function, after obtaining the generated output of the various distributed power sources,
Also include：
According to the generated output of the various described distributed power source for obtaining, respectively to the various distributed electricals in the micro-capacitance sensor
Source is controlled.
6. a kind of GPU processors, it is characterised in that include：
Object function arranging unit, for arranging the energy hole object function of micro-capacitance sensor；Wherein, the energy hole target letter
Include control variables in number, the control variables includes the generated output of various distributed power sources in the micro-capacitance sensor；
Parameter initialization unit is executed, for initializing the execution parameter of default population film algorithm；Wherein, the default particle
Comprising multilayer underlying membrane and one layer of top layer film in group's film algorithm, and the parameter that executes includes the number of plies of underlying membrane, described basic
The corresponding iterations threshold value of film, the corresponding iterations threshold value of the top layer film, the top layer film current iteration number of times, described
The number of particle, the position of each particle and speed in population film algorithm；
Fitness function signal generating unit, for according to the energy hole object function, generating the suitable of the population film algorithm
Response function；
Alternative particle acquiring unit, for there is position and the particle of speed to be randomly assigned to each layer underlying membrane each
In, and according to the fitness function and the corresponding iterations threshold value of the underlying membrane, to particle on underlying membrane per layer described
Speed and position be updated, with obtain per layer of underlying membrane on alternative optimal particle；Wherein, per layer of underlying membrane
In include particle described at least one；
Intended particle acquiring unit, for each alternative optimal particle described is sent in the top layer film, on the top layer
Target optimal particle is determined in film；Wherein, the target optimal particle has optimal location value；
End condition judging unit, corresponding repeatedly for judging whether the top layer film current iteration number of times reaches the top layer film
For frequency threshold value；If so, control variables determining unit is triggered, otherwise, alternative particle updating block is triggered；
Control variables determining unit, for being defined as the energy hole target by the optimal location value of the target optimal particle
The optimal solution of function, so that obtain the generated output of the various distributed power sources；
Alternative particle updating block, for according to the target optimal particle, returning the speed to particle on underlying membrane per layer described
Degree and position are updated, to regain the alternative optimal particle on per layer of underlying membrane.
7. a kind of microgrid energy control system, it is characterised in that including CPU, parameter collection module, as claimed in claim 6
GPU processors and micro-capacitance sensor, wherein：
The CPU, for initializing the speed of each particle and position in population film algorithm, and each particle described is sent
To the GPU；
The parameter collection module, for sending the operational factor of multiple distributed power sources in the micro-capacitance sensor of collection to institute
State GPU；
The GPU, for according to the operational factor, arranging the energy hole object function of the micro-capacitance sensor, and according to each
The initialized particle, executes default population film algorithm to obtain in the energy hole object function control variables most
Excellent solution, and the optimal solution of the control variables is sent to the CPU；Wherein, the control variables includes the various distributions
The generated output of formula power supply；
The CPU, multiple distributed power sources in being additionally operable to the optimal solution according to the control variables to the micro-capacitance sensor send out
Electrical power is controlled.
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