CN107590607A - A kind of micro-capacitance sensor Optimal Scheduling and method based on photovoltaic prediction - Google Patents

A kind of micro-capacitance sensor Optimal Scheduling and method based on photovoltaic prediction Download PDF

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CN107590607A
CN107590607A CN201710845015.1A CN201710845015A CN107590607A CN 107590607 A CN107590607 A CN 107590607A CN 201710845015 A CN201710845015 A CN 201710845015A CN 107590607 A CN107590607 A CN 107590607A
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mrow
msubsup
micro
capacitance sensor
photovoltaic
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周开乐
李鹏涛
杨善林
崔洪波
孙莉
陆信辉
温露露
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Hefei University of Technology
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Hefei University of Technology
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a kind of micro-capacitance sensor Optimal Scheduling based on photovoltaic prediction and method, its feature to include：Data processing unit, state of electric distribution network monitoring unit, information of vehicles module, management and running unit；Data processing unit includes：Data memory module, photovoltaic power generation output forecasting module；Data memory module includes：History photovoltaic data memory module, history meteorological data memory module, scheduling day meteorological data memory module, information of vehicles module include information of vehicles recording module, charge requirement computing module.The present invention carries out micro-capacitance sensor Optimized Operation based on photovoltaic prediction data, can effectively reduce what is brought by photovoltaic itself randomness with uncertainty to scheduling, improve the utilization ratio of photovoltaic energy, reduce micro-capacitance sensor operating cost.

Description

A kind of micro-capacitance sensor Optimal Scheduling and method based on photovoltaic prediction
Technical field
The invention belongs to micro-capacitance sensor scheduling field, specifically a kind of micro-capacitance sensor Optimized Operation system based on photovoltaic prediction System and method.
Background technology
As government carries forward vigorously to ev industry in recent years, electric automobile is gradually being popularized.It is but electronic at present The energy recharge of automobile mainly based on electric energy caused by non-renewable energy resources, fails really to realize the cleaning of electric car electric energy Substitute.
The continuous development of photovoltaic technology, the photovoltaic scale in China constantly expand, and photovoltaic generating system application is also more extensive, Electric automobile charging station comprising photovoltaic cells also gradually rises.Photovoltaic can effectively reduce people as a kind of new cleaning fuel Dependence of the class society to fossil energy, and meet the charge requirement of electric automobile substituting traditional energy to a certain degree, but by There is randomness and complicated uncertainty in photovoltaic generation, photovoltaic is dissolved still in relatively low level.
The Optimization Scheduling of existing electric automobile access micro-capacitance sensor does not often exchange interior each moment photovoltaic of subsisting Unit generated output is predicted, and can not understand the power generation characteristics for dispatching in a few days photovoltaic cells.And photovoltaic generation have it is larger Randomness and complicated uncertainty, can give the scheduling of actual charging electric vehicle to bring various problems.Therefore need more comprehensive Understanding photovoltaic cells power generation characteristics, it is maximized to improve photovoltaic utilization rate in micro-capacitance sensor, reduce the operating cost of charging station.
The content of the invention
The present invention is to solve above-mentioned the shortcomings of the prior art part, propose a kind of micro- electricity based on photovoltaic prediction Net Optimized Operation system and method, to can accurately understand scheduling in a few days photovoltaic generation characteristic so as to improving the profit of photovoltaic generation With rate, the operation cost of reduction charging station micro-capacitance sensor.
The present invention adopts the following technical scheme that to solve technical problem：
A kind of the characteristics of micro-capacitance sensor Optimal Scheduling based on photovoltaic prediction of the present invention, is applied to by photovoltaic cells, micro- In the micro-capacitance sensor that type gas turbine group, charging pile and bulk power grid are formed, it is charging electric vehicle that the micro-capacitance sensor, which is used for, described System includes：Data processing unit, state of electric distribution network monitoring unit, information of vehicles module, management and running unit；
The data processing unit includes：Data memory module, photovoltaic power generation output forecasting module；
The data memory module includes：History photovoltaic data memory module, history meteorological data memory module, dispatch day Meteorological data memory module；
The history generated output data of the history photovoltaic data memory module collection photovoltaic cells simultaneously pass to the light Lie prostrate output prediction module；
The history meteorological data of history meteorological data memory module collection photovoltaic cells location simultaneously passes to the light Lie prostrate output prediction module；
The photovoltaic power generation output forecasting module uses the history photovoltaic data message and history meteorological data training prediction mould Type；
The scheduling day meteorological data memory module collection photovoltaic cells location scheduling day weather forecast data, and pass Pass photovoltaic power generation output forecasting module；
The scheduling day weather forecast number that the photovoltaic power generation output forecasting module inputs according to scheduling day meteorological data memory module According to photovoltaic cells scheduling day generated output is calculated, and passes to the management and running unit；
The information of vehicles module includes information of vehicles recording module, charge requirement computing module；
The information of vehicles recording module obtains the battery charge state information of electric automobile and passes to the charging and needs Seek computing module；
The charge requirement computing module obtains electric automobile access micro-capacitance sensor and leaves the micro-capacitance sensor time, and according to described Battery charge state information, the management and running unit is passed to after calculating charging electric vehicle demand；
The management and running unit is established with photovoltaic utilization rate maximum and the minimum target of total operating cost of micro-capacitance sensor Micro-capacitance sensor Optimal Operation Model, and day generated output, charging electric vehicle demand are dispatched to described micro- according to the photovoltaic cells Optimal dispatch model is solved, obtain charging electric vehicle power, micro-gas-turbine unit contribute and bulk power grid to Micro-capacitance sensor sale of electricity power, so as to realize the Optimized Operation of micro-capacitance sensor；
The state of electric distribution network monitoring unit is used for the running status for monitoring photovoltaic cells, charging pile and bulk power grid, and Alarmed when the photovoltaic cells, charging pile or bulk power grid break down.
The present invention it is a kind of based on photovoltaic prediction micro-capacitance sensor Optimization Scheduling the characteristics of be applied to by photovoltaic cells PV, In the micro-capacitance sensor that micro-gas-turbine unit MT, charging pile and bulk power grid are formed, and carry out as follows：
Step 1, it is determined that being adjusted using electric automobile, photovoltaic cells PV, micro-gas-turbine unit MT as micro-capacitance sensor optimization is participated in Spend unit；
Step 2, predict that the photovoltaic cells PV is dispatching the generated output value of in a few days t
Step 3, establish the micro-capacitance sensor of the minimum target of total operating cost based on photovoltaic utilization rate maximum and micro-capacitance sensor Optimized Operation object function, and constraints is determined, so as to build micro-capacitance sensor Optimal Operation Model；
Step 4, charging electric vehicle demand data is obtained, including：Electric automobile under tou power price accesses micro-capacitance sensor Time started, time departure, charge volume needed for kth electric automobile；
Step 5, the micro-capacitance sensor Optimal Operation Model is solved using genetic algorithm, draw and dispatching the electricity of in a few days t Electrical automobile charge power, micro-gas-turbine unit MT power outputs, bulk power grid are to micro-capacitance sensor sale of electricity power.
The characteristics of prediction micro-capacitance sensor dispatching method of the present invention based on photovoltaic, lies also in,
The step 2 is carried out as follows：
Step 1, data acquisition：
History generated output data, the history generated output data for obtaining the photovoltaic cells PV correspond to the light at moment Unit PV locations history meteorological data is lied prostrate, the history meteorological data includes：Day type, intensity of solar radiation, temperature, sky Makings amount；
Step 2, model training：
The data of step 2.1, the abnormal data in the rejecting history generated output data and inactivity output period, And the data to lacking are filled up, so as to obtain pretreated history generated output data, the inactivity output period is rejected History meteorological data, so as to obtain pretreated history meteorological data；
Step 2.2, the pretreated history generated output data and history meteorological data are normalized, And the data after normalization are established into training sample set, simultaneously forecast model, institute are trained to neutral net using training sample State forecast model and establish four submodels by season, each season submodel is classified according to day type；
Step 3, scheduling day photovoltaic power generation power prediction：
By the weather forecast data input for dispatching day into the forecast model, adjusted so as to predict to obtain photovoltaic cells PV The generated output value for interior t of subsisting
Micro-capacitance sensor Optimized Operation object function in the step 3 is,
In formula (1), F is micro-capacitance sensor total operating cost object function,Represent t bulk power grid and microgrid power Swap status, andT bulk power grid is represented not to micro-capacitance sensor sale of electricity,Represent that t is big Power network to micro-capacitance sensor sale of electricity,T bulk power grid electricity price is represented,Represent t micro-gas-turbine unit MT work shape State, andRepresent that t micro-gas-turbine unit MT does not have power output,Represent that t is miniature Gas turbine group MT has power output,T micro-gas-turbine unit MT power output is represented,Represent that t is miniature Gas turbine group MT unit cost of electricity-generating,Micro-gas-turbine unit MT start-up cost is represented,It is expressed as miniature combustion Turbine unit MT shutdown cost,Represent t bulk power grid to micro-capacitance sensor sale of electricity power；
Obtain losing F caused by the photovoltaic cells PV abandons light using formula (2)Pv, a：
In formula (2), CaThe penalty coefficient of light is abandoned in expression, and T represents micro-capacitance sensor scheduling day total run time, TmtRepresent miniature combustion The working time of turbine unit,The total load of t micro-capacitance sensor is represented,Represent the circuit loss of t microgrid；
The micro-capacitance sensor Optimized Operation object function as shown in formula (3) is collectively formed as formula (1) and formula (2)：
Min C=F+FPv, a (3)。
Constraints in the step 3 is：
In formula (4),Micro-gas-turbine unit MT peak power output is represented,Represent micro-capacitance sensor allow with The maximum exchange power of bulk power grid,Represent the total load of t micro-capacitance sensor, PRRepresent the spinning reserve power of micro-capacitance sensor；
In formula (6),Micro-gas-turbine unit MT peak power output is represented,Represent micro-gas-turbine unit MT minimum output power；
In formula (7),Micro-gas-turbine unit MT minimum climbing power is represented,Represent miniature gas turbine Group MT maximum climbing power；
In formula (8),WithThe limits value up and down that micro-capacitance sensor interacts with bulk power grid power is represented respectively；
In formula (9), SOCkKth electric automobile state-of-charge is represented,The electronic vapour of kth is represented respectively Car carrying capacity bound；
In formula (10),Represent minimum total charge power of t electric automobile, NtRepresent that t participates in scheduling Electric automobile quantity,Represent the maximum charge power of charging pile；
In formula (11),Represent maximum total charge power of t electric automobile, NZRepresent charging pile in micro-capacitance sensor Quantity；
In formula (12),Represent total charge power during t electric automobile access micro-capacitance sensor；
In formula (13), KtThe electric automobile quantity of t access micro-capacitance sensor is represented,Represent that all accesses of t are micro- The charge power of kth electric automobile in the electric automobile of power network.
Compared with prior art, the beneficial effects of the present invention are：
1st, brought when the Optimal Scheduling that the present invention designs integrally can effectively reduce electric automobile access power network to power network Impact, improve photovoltaic utilization rate, reduce micro-capacitance sensor operating cost；And alarm mechanism is established, there is event in micro-capacitance sensor each unit Alarm is sent for administrative staff during barrier, improves the stationarity of operation of power networks；
2nd, forecast model is established and trained in the present invention using historical data, is adjusted according to scheduling day weather forecast data prediction Subsist each moment photovoltaic generation power of interior day photovoltaic cells, effectively reduce the randomness of photovoltaic generation and uncertain to real Influence caused by during the micro-capacitance sensor Optimized Operation of border；
3rd, the present invention considered in micro-capacitance sensor total operating cost abandon light caused by loss, photovoltaic generation can be effectively improved Utilization rate, reduce the operating cost of micro-capacitance sensor.
Brief description of the drawings
Fig. 1 is the system construction drawing of the present invention.
Embodiment
In the present embodiment, a kind of micro-capacitance sensor Optimal Scheduling based on photovoltaic prediction is applied to by photovoltaic cells, micro- In the micro-capacitance sensor that type gas turbine group, charging pile are formed, it is charging electric vehicle that micro-capacitance sensor, which is used for,.
In the present invention Optimal Scheduling structure as shown in figure 1, including：Data processing unit, state of electric distribution network monitoring Unit, information of vehicles module, management and running unit；
Data processing unit includes：Data memory module, photovoltaic power generation output forecasting module；
Data memory module includes：History photovoltaic data memory module, history meteorological data memory module, scheduling day are meteorological Data memory module；
The history generated output data of history photovoltaic data memory module collection photovoltaic cells simultaneously pass to photovoltaic output in advance Survey module；
History meteorological data memory module gathers the history meteorological data of photovoltaic cells location and passes to photovoltaic Power prediction module；
Photovoltaic power generation output forecasting module usage history photovoltaic data message and history meteorological data training forecast model；
Day meteorological data memory module collection photovoltaic cells location scheduling day weather forecast data are dispatched, and are passed to Photovoltaic power generation output forecasting module；
The scheduling day weather forecast data that photovoltaic power generation output forecasting module inputs according to scheduling day meteorological data memory module, meter Calculation obtains photovoltaic cells scheduling day generated output, and passes to management and running unit；
Information of vehicles module includes information of vehicles recording module, charge requirement computing module；
Information of vehicles module obtains the battery charge state information of electric automobile and passes to charge requirement computing module；
Charge requirement computing module obtains electric automobile access micro-capacitance sensor and leaves the micro-capacitance sensor time, and according to battery charge Status information, management and running unit is passed to after calculating charging electric vehicle demand；
Management and running unit establishes micro- electricity with photovoltaic utilization rate maximum and the minimum target of total operating cost of micro-capacitance sensor Net Optimal Operation Model, and day generated output, charging electric vehicle demand are dispatched to micro-capacitance sensor Optimized Operation according to photovoltaic cells Model is solved, and obtains charging electric vehicle power, micro-gas-turbine unit output and bulk power grid to micro-capacitance sensor sale of electricity work( Rate, so as to realize the Optimized Operation of micro-capacitance sensor；
State of electric distribution network monitoring unit is used for the running status for monitoring photovoltaic cells, charging pile and bulk power grid, and in light Volt unit, charging pile or bulk power grid are alarmed when breaking down.
In the present embodiment, a kind of micro-capacitance sensor Optimization Scheduling based on photovoltaic prediction, applied to by photovoltaic cells PV, micro- In the micro-capacitance sensor that type gas turbine group MT, charging pile are formed, and carry out as follows：
Step 1, it is determined that being adjusted using electric automobile, photovoltaic cells PV, micro-gas-turbine unit MT as micro-capacitance sensor optimization is participated in The unit of degree；
Step 2, prediction photovoltaic cells PV are dispatching the generated output value of in a few days t
Photovoltaic power is predicted forms training set based on photovoltaic generation power data and same period Historical Meteorological Information, and uses and be somebody's turn to do Training set trains neutral net, will dispatch the neutral net after day weather forecast data input is trained, obtains dispatching day photovoltaic list First generated power forecasting result, it is comprised the following steps that：
Step 1, data acquisition：
The history generated output data that photovoltaic cells PV goes over 1 year are obtained, data resolution 15min, obtain history hair Electrical power data correspond to the photovoltaic cells PV locations history meteorological data at moment, and history meteorological data includes：Day type, too Positive radiation intensity, temperature, air quality；Wherein day type is broadly divided into following a few classes：Fine day, cloudy, cloudy (haze), rain (snow)；Photovoltaic cells generated output major influence factors are intensity of solar radiation, atmospheric temperature, air quality, photovoltaic cells turn Change efficiency, photovoltaic module temperature etc., mainly consider in this model intensity of solar radiation, temperature, air quality three it is main because Element.
Step 2, model training：
Step 2.1, the abnormal data in rejecting history generated output data and inactivity output period data, and according to Formula (1) is filled up to the data lacked, so as to obtain pretreated history generated output data, when rejecting inactivity output The history meteorological data of section, so as to obtain pretreated history meteorological data；
In formula (1), PtRepresent t missing data, Pt-1Represent t-1 moment photovoltaic cells generated outputs, Pt+1Represent t+1 Moment photovoltaic cells generated output,Represent proxima luce (prox. luc) t photovoltaic cells generated output, α1, α2, α3Expression, which is filled up, respectively is Number；
Because different regions sunrise is different from light application time, according to photovoltaic cells history generated output data, photovoltaic is set Unit is in daily Tst-TendPeriod is the power output period, daily Tend- next day TstPeriod is that inactivity exports period, the period Interior photovoltaic cells power output is 0.
Step 2.2, pretreated history generated output data and history meteorological data are normalized according to formula (2) Processing, the data after being normalized, including history photovoltaic data and history meteorological data,
In formula (2), X represents the data after normalization, and x represents the data before normalization, xminVariable x minimum value is represented, xmaxRepresent variable x maximum；
Data after normalization are established into training sample set, is trained using training sample neutral net and establishes prediction Model, forecast model establish four submodels by season, and each season submodel is classified according to day type, is divided into a day class pattern Type；
Step 3, scheduling day photovoltaic power generation power prediction：
According to the Sino-Japan type of weather forecast data, the weather forecast data input of the day current season into forecast model will be dispatched Under knot model in corresponding day Type model, the generated output value of in a few days t is being dispatched so as to predict to obtain photovoltaic cells PV
Step 3, establish the micro-capacitance sensor of the minimum target of total operating cost based on photovoltaic utilization rate maximum and micro-capacitance sensor Optimized Operation object function, and constraints is determined, so as to build micro-capacitance sensor Optimal Operation Model；
In formula (3), F is micro-capacitance sensor total operating cost object function,Represent t bulk power grid and microgrid power Swap status, andT bulk power grid is represented not to micro-capacitance sensor sale of electricity,Represent that t is big Power network to micro-capacitance sensor sale of electricity,T bulk power grid electricity price is represented,Represent t micro-gas-turbine unit MT work shape State, andRepresent that t micro-gas-turbine unit MT does not have power output,Represent that t is miniature Gas turbine group MT has power output,T micro-gas-turbine unit MT power output is represented,Represent that t is miniature Gas turbine group MT unit cost of electricity-generating,Micro-gas-turbine unit MT start-up cost is represented,It is expressed as miniature combustion Turbine unit MT shutdown cost,Represent t bulk power grid to micro-capacitance sensor sale of electricity power；
When t photovoltaic cells generated energy is more than micro-capacitance sensor total capacity requirement, and photovoltaic cells reach the regulating power upper limit, Then need to abandon light guarantee micro-capacitance sensor power supply and balancing the load.Obtain losing F caused by photovoltaic cells PV abandons light using formula (4)Pv, a：
In formula (4), CaThe penalty coefficient of light is abandoned in expression, and T represents micro-capacitance sensor scheduling day total run time, TmtRepresent miniature combustion The working time of turbine unit,The total load of t micro-capacitance sensor is represented,Represent the circuit loss of t microgrid；
To improve photovoltaic utilization rate, lose and be included in charging station micro-capacitance sensor total operating cost as caused by abandoning light, by formula (3) The micro-capacitance sensor Optimized Operation object function as shown in formula (5) is collectively formed with formula (4)：
Min C=F+FPv, a (5)
Decision variable is：T kth charging electric vehicle powerMicro-gas-turbine unit MT power outputsBulk power grid is to micro-capacitance sensor sale of electricity power
Constraints is：
In formula (6),Micro-gas-turbine unit MT peak power output is represented,Represent micro-capacitance sensor allow with The maximum exchange power of bulk power grid,Represent the total load of t micro-capacitance sensor, PRRepresent the spinning reserve power of micro-capacitance sensor；
In formula (8),Micro-gas-turbine unit MT peak power output is represented,Represent micro-gas-turbine unit MT minimum output power；
In formula (9),Micro-gas-turbine unit MT minimum climbing power is represented,Represent miniature gas turbine Group MT maximum climbing power；
In formula (10),WithThe limits value up and down that micro-capacitance sensor interacts with bulk power grid electric energy is represented respectively；
In formula (11), SOCkKth electric automobile state-of-charge is represented,Represent that kth is electronic respectively Automobile carrying capacity bound；
In formula (12),Represent minimum total charge power of t electric automobile, NtRepresent that t participates in scheduling Electric automobile quantity,Represent the maximum charge power of charging pile；
In formula (13),Represent maximum total charge power of t electric automobile, NZRepresent charging pile in micro-capacitance sensor Quantity；
In formula (14),Represent total charge power during t electric automobile access micro-capacitance sensor；
In formula (15), KtThe electric automobile quantity of t access micro-capacitance sensor is represented,Represent that all accesses of t are micro- The charge power of kth electric automobile in the electric automobile of power network.
Step 4, charging electric vehicle demand data is obtained, including：Electric automobile under tou power price accesses micro-capacitance sensor Time started, time departure, charge volume needed for kth electric automobile；
It is determined that access micro-capacitance sensor participates in the electric automobile quantity N of scheduling, kth electric automobile access micro-capacitance sensor timeWith Family setting electric automobile leaves the micro-capacitance sensor timeBattery charge state when accessing micro-capacitance sensorMeet user's trip requirements Default electric automobile it is leaving from station when battery charge stateThe charge volume as needed for formula (14) calculates kth electric automobile
In formula (16),Represent kth electric automobile t charge power；
Step 5, micro-capacitance sensor Optimal Operation Model is solved using genetic algorithm, draw and dispatching the electronic vapour of in a few days t Car charge power, micro-gas-turbine unit MT power outputs, bulk power grid are to micro-capacitance sensor sale of electricity power.
The tou power price strategy of the bulk power grid accessed in the Optimization Scheduling according to micro-capacitance sensor, mould is used as using genetic algorithm Type method for solving, specific steps include：
Step 5.1, population is initialized：The Population for setting genetic algorithm is 20, the initial population as initiating searches point Volume data
Step 5.2, fitness function：Directly fitted using object function as fitness function, i.e., each chromosome It is exactly target function value to answer angle value, and fitness value is genetic to follow-on select probability as individual；
Step 5.3, selected using wheel disc mechanism, determine the selected number of each individual
Step 5.4, intersect：It is 0.6 to set crossing-over rate
Step 5.5, make a variation：Row variation is entered to chromosome structure, it is changed original structure, reaches the mesh of emergent evolution , aberration rate is arranged to 0.1
Maximum iteration is 100, because genetic algorithm belongs to heuristic search algorithm, therefore each optimum results in itself All just there is randomness, therefore each result takes 20 times and computes repeatedly to obtain average value.

Claims (5)

  1. A kind of 1. micro-capacitance sensor Optimal Scheduling based on photovoltaic prediction, it is characterized in that applied to by photovoltaic cells, miniature gas In the micro-capacitance sensor that wheel unit, charging pile and bulk power grid are formed, the micro-capacitance sensor is used to be charging electric vehicle, the system bag Include：Data processing unit, state of electric distribution network monitoring unit, information of vehicles module, management and running unit；
    The data processing unit includes：Data memory module, photovoltaic power generation output forecasting module；
    The data memory module includes：History photovoltaic data memory module, history meteorological data memory module, scheduling day are meteorological Data memory module；
    The history photovoltaic data memory module gathers the history generated output data of photovoltaic cells and passes to the photovoltaic Power prediction module；
    History meteorological data memory module gathers the history meteorological data of photovoltaic cells location and passes to the photovoltaic Power prediction module；
    The photovoltaic power generation output forecasting module uses the history photovoltaic data message and history meteorological data training forecast model；
    The scheduling day meteorological data memory module collection photovoltaic cells location scheduling day weather forecast data, and pass to Photovoltaic power generation output forecasting module；
    The scheduling day weather forecast data that the photovoltaic power generation output forecasting module inputs according to scheduling day meteorological data memory module, meter Calculation obtains photovoltaic cells scheduling day generated output, and passes to the management and running unit；
    The information of vehicles module includes information of vehicles recording module, charge requirement computing module；
    The information of vehicles recording module obtains the battery charge state information of electric automobile and passes to the charge requirement meter Calculate module；
    The charge requirement computing module obtains electric automobile access micro-capacitance sensor and leaves the micro-capacitance sensor time, and according to the battery State of charge information, the management and running unit is passed to after calculating charging electric vehicle demand；
    The management and running unit establishes micro- electricity with photovoltaic utilization rate maximum and the minimum target of total operating cost of micro-capacitance sensor Net Optimal Operation Model, and day generated output, charging electric vehicle demand are dispatched to the micro-capacitance sensor according to the photovoltaic cells Optimal Operation Model is solved, and obtains charging electric vehicle power, micro-gas-turbine unit output and bulk power grid to micro- electricity Net sale of electricity power, so as to realize the Optimized Operation of micro-capacitance sensor；
    The state of electric distribution network monitoring unit is used for the running status for monitoring photovoltaic cells, charging pile and bulk power grid, and in institute State when photovoltaic cells, charging pile or bulk power grid break down and alarmed.
  2. A kind of 2. micro-capacitance sensor Optimization Scheduling based on photovoltaic prediction, it is characterized in that applied to by photovoltaic cells PV, miniature combustion In the micro-capacitance sensor that turbine unit MT, charging pile and bulk power grid are formed, and carry out as follows：
    Step 1, it is determined that being used as participation micro-capacitance sensor Optimized Operation list using electric automobile, photovoltaic cells PV, micro-gas-turbine unit MT Member；
    Step 2, predict that the photovoltaic cells PV is dispatching the generated output value of in a few days t
    Step 3, establish the micro-capacitance sensor optimization of the minimum target of total operating cost based on photovoltaic utilization rate maximum and micro-capacitance sensor Regulation goal function, and constraints is determined, so as to build micro-capacitance sensor Optimal Operation Model；
    Step 4, charging electric vehicle demand data is obtained, including：The beginning of electric automobile access micro-capacitance sensor under tou power price Time, time departure, charge volume needed for kth electric automobile；
    Step 5, the micro-capacitance sensor Optimal Operation Model is solved using genetic algorithm, draw and dispatching the electronic vapour of in a few days t Car charge power, micro-gas-turbine unit MT power outputs, bulk power grid are to micro-capacitance sensor sale of electricity power.
  3. It is 3. according to claim 2 based on photovoltaic prediction micro-capacitance sensor dispatching method, it is characterised in that the step 2 is by such as Lower step is carried out：
    Step 1, data acquisition：
    Obtain the history generated output data of the photovoltaic cells PV, the history generated output data correspond to the photovoltaic list at moment First PV locations history meteorological data, the history meteorological data include：Day type, intensity of solar radiation, temperature, air matter Amount；
    Step 2, model training：
    The data of step 2.1, the abnormal data in the rejecting history generated output data and inactivity output period, and it is right The data lacked are filled up, and so as to obtain pretreated history generated output data, reject going through for inactivity output period History meteorological data, so as to obtain pretreated history meteorological data；
    Step 2.2, the pretreated history generated output data and history meteorological data are normalized, and will Data after normalization establish training sample set, are trained simultaneously forecast model to neutral net using training sample, described pre- Survey model and establish four submodels by season, each season submodel is classified according to day type；
    Step 3, scheduling day photovoltaic power generation power prediction：
    By the weather forecast data input for dispatching day into the forecast model, day is being dispatched so as to predict to obtain photovoltaic cells PV The generated output value of interior t
  4. 4. the micro-capacitance sensor Optimization Scheduling according to claim 2 based on photovoltaic prediction, it is characterised in that the step Micro-capacitance sensor Optimized Operation object function in three is,
    <mrow> <mi>F</mi> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <mo>&amp;lsqb;</mo> <msubsup> <mi>u</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>t</mi> </msubsup> <msubsup> <mi>C</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>t</mi> </msubsup> <mo>+</mo> <msubsup> <mi>u</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>t</mi> </msubsup> <msubsup> <mi>P</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>t</mi> </msubsup> <msubsup> <mi>C</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>t</mi> </msubsup> <mo>+</mo> <msubsup> <mi>C</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mrow> <mi>s</mi> <mi>t</mi> <mi>a</mi> <mi>r</mi> <mi>t</mi> </mrow> </msubsup> <mo>&amp;times;</mo> <mi>max</mi> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <msubsup> <mi>u</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>t</mi> </msubsup> <mi>-</mi> <msubsup> <mi>u</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>C</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mrow> <mi>s</mi> <mi>h</mi> <mi>u</mi> <mi>t</mi> </mrow> </msubsup> <mo>&amp;times;</mo> <mi>min</mi> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <msubsup> <mi>u</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>t</mi> </msubsup> <mo>-</mo> <msubsup> <mi>u</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>t</mi> </msubsup> <msubsup> <mi>C</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>t</mi> </msubsup> <mo>&amp;rsqb;</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
    In formula (1), F is micro-capacitance sensor total operating cost object function,Expression t bulk power grid exchanges with microgrid power State, andT bulk power grid is represented not to micro-capacitance sensor sale of electricity,Represent t bulk power grid To micro-capacitance sensor sale of electricity,T bulk power grid electricity price is represented,T micro-gas-turbine unit MT working condition is represented, andRepresent that t micro-gas-turbine unit MT does not have power output,Represent t miniature gas Wheel unit MT has power output,T micro-gas-turbine unit MT power output is represented,Represent t miniature gas Unit MT unit cost of electricity-generating is taken turns,Micro-gas-turbine unit MT start-up cost is represented,It is expressed as micro-gas-turbine Unit MT shutdown cost,Represent t bulk power grid to micro-capacitance sensor sale of electricity power；
    Obtain losing F caused by the photovoltaic cells PV abandons light using formula (2)Pv, a：
    <mrow> <msub> <mi>F</mi> <mrow> <mi>p</mi> <mi>v</mi> <mo>,</mo> <mi>a</mi> </mrow> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <msub> <mi>C</mi> <mi>a</mi> </msub> <mrow> <mo>(</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>T</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> </msub> </munderover> <msubsup> <mi>P</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>t</mi> </msubsup> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>p</mi> <mi>v</mi> </mrow> <mi>t</mi> </msubsup> <mo>-</mo> <msubsup> <mi>P</mi> <mrow> <mi>L</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> </mrow> <mi>t</mi> </msubsup> <mo>-</mo> <msubsup> <mi>P</mi> <mrow> <mi>L</mi> <mi>o</mi> <mi>s</mi> <mi>e</mi> </mrow> <mi>t</mi> </msubsup> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
    In formula (2), CaThe penalty coefficient of light is abandoned in expression, and T represents micro-capacitance sensor scheduling day total run time, TmtRepresent micro-gas-turbine The working time of unit,The total load of t micro-capacitance sensor is represented,Represent the circuit loss of t microgrid；
    The micro-capacitance sensor Optimized Operation object function as shown in formula (3) is collectively formed as formula (1) and formula (2)：
    Min C=F+FPv, a (3)。
  5. 5. the micro-capacitance sensor dispatching method according to claim 2 based on photovoltaic prediction, it is characterised in that in the step 3 Constraints be：
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>max</mi> </msubsup> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>max</mi> </msubsup> <mo>&amp;GreaterEqual;</mo> <msubsup> <mi>P</mi> <mrow> <mi>L</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> </mrow> <mi>t</mi> </msubsup> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>L</mi> <mi>o</mi> <mi>s</mi> <mi>e</mi> </mrow> <mi>t</mi> </msubsup> <mo>+</mo> <msub> <mi>P</mi> <mi>R</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>p</mi> <mi>v</mi> </mrow> <mi>t</mi> </msubsup> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>t</mi> </msubsup> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>t</mi> </msubsup> <mo>=</mo> <msubsup> <mi>P</mi> <mrow> <mi>L</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> </mrow> <mi>t</mi> </msubsup> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>L</mi> <mi>o</mi> <mi>s</mi> <mi>e</mi> </mrow> <mi>t</mi> </msubsup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>t</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>m</mi> <mi>max</mi> </msubsup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>&amp;Delta;P</mi> <mi>m</mi> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <mo>|</mo> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> </msubsup> <mo>-</mo> <msubsup> <mi>P</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>t</mi> </msubsup> </mrow> <mo>|</mo> <mo>&amp;le;</mo> <msubsup> <mi>&amp;Delta;P</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> <mi>max</mi> </msubsup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>t</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>max</mi> </msubsup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>SOC</mi> <mi>k</mi> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <msub> <mi>SOC</mi> <mi>k</mi> </msub> <mo>&amp;le;</mo> <msubsup> <mi>SOC</mi> <mi>k</mi> <mi>max</mi> </msubsup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mi>z</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>N</mi> <mi>t</mi> </msub> <mo>&amp;times;</mo> <msubsup> <mi>P</mi> <mrow> <mi>E</mi> <mi>V</mi> </mrow> <mi>max</mi> </msubsup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mi>Z</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> <mo>&amp;lsqb;</mo> <msub> <mi>N</mi> <mi>t</mi> </msub> <mo>&amp;times;</mo> <msubsup> <mi>P</mi> <mrow> <mi>E</mi> <mi>V</mi> </mrow> <mi>max</mi> </msubsup> <mo>,</mo> <msub> <mi>N</mi> <mi>Z</mi> </msub> <mo>&amp;times;</mo> <msubsup> <mi>P</mi> <mrow> <mi>E</mi> <mi>V</mi> </mrow> <mi>max</mi> </msubsup> <mo>&amp;rsqb;</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mi>z</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>E</mi> <mi>V</mi> </mrow> <mi>t</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>Z</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>12</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>E</mi> <mi>V</mi> </mrow> <mi>t</mi> </msubsup> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>K</mi> <mi>t</mi> </msub> </munderover> <msubsup> <mi>P</mi> <mrow> <mi>E</mi> <mi>V</mi> </mrow> <mi>k</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow>
    In formula (4),Micro-gas-turbine unit MT peak power output is represented,Represent micro-capacitance sensor allow with big electricity The maximum exchange power of net,Represent the total load of t micro-capacitance sensor, PRRepresent the spinning reserve power of micro-capacitance sensor；
    In formula (6),Micro-gas-turbine unit MT peak power output is represented,Represent micro-gas-turbine unit MT most Small power output；
    In formula (7),Micro-gas-turbine unit MT minimum climbing power is represented,Represent micro-gas-turbine unit MT Maximum climbing power；
    In formula (8),WithThe limits value up and down that micro-capacitance sensor interacts with bulk power grid power is represented respectively；
    In formula (9), SOCkKth electric automobile state-of-charge is represented,Kth electric automobile lotus is represented respectively Electricity bound；
    In formula (10),Represent minimum total charge power of t electric automobile, NtRepresent that t participates in the electronic of scheduling Automobile quantity,Represent the maximum charge power of charging pile；
    In formula (11),Represent maximum total charge power of t electric automobile, NZRepresent charging pile quantity in micro-capacitance sensor；
    In formula (12),Represent total charge power during t electric automobile access micro-capacitance sensor；
    In formula (13), KtThe electric automobile quantity of t access micro-capacitance sensor is represented,Represent all access micro-capacitance sensors of t Electric automobile in kth electric automobile charge power.
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