CN107370170A - A kind of energy storage system capacity collocation method for considering capacity price of electricity and load prediction error - Google Patents

A kind of energy storage system capacity collocation method for considering capacity price of electricity and load prediction error Download PDF

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CN107370170A
CN107370170A CN201710488425.5A CN201710488425A CN107370170A CN 107370170 A CN107370170 A CN 107370170A CN 201710488425 A CN201710488425 A CN 201710488425A CN 107370170 A CN107370170 A CN 107370170A
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CN107370170B (en
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杨秦敏
李越
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陈积明
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Zhejiang University ZJU
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention discloses a kind of energy storage system capacity collocation method for considering capacity price of electricity and load prediction error, belong to technical field of power systems.This method is based on distributed energy management system, local energy-storage system battery is divided into for predicting that load optimizes the Optimized Operation part of scheduling and emergent part for optimizing compensation to uncertain load caused by load prediction error a few days ago, by introducing capacity price of electricity fee system, energy-storage system economy model and optimal discharge and recharge scheduling strategy, the optimal capacity configuration of energy-storage system is carried out as optimization aim to minimize the overall average daily cost of unique user.The present invention proposes a kind of combination traversal iteration theory and the optimized algorithm of dual-stage optimum theory, and two benches battery capacity Optimization Solution is described respectively using MILP model.For a user, the science and accuracy of energy storage system capacity collocation method are improved, there is important scientific meaning and application value to the research promotion of energy-storage system.

Description

A kind of energy storage system capacity collocation method for considering capacity price of electricity and load prediction error
Technical field
Consider that the probabilistic energy-storage system of load forecast holds the present invention relates to a kind of distributed energy management system Collocation method is measured, by introducing capacity price of electricity fee system, energy-storage system economy model and optimal discharge and recharge scheduling strategy, most The average daily cost of smallization user, towards the optimization problem of urban distribution network user terminal, belongs to field of power.
Background technology
With the development of social economy and the continuous improvement of living standards of the people, load shows peak valley and born in power system The characteristics of lotus difference increases year by year, and number of working hours based on maximum load declines year by year.The continuous growth of load causes the peak of load Paddy difference increasingly increases, and then causes the supply and demand of power system unbalanced phenomenon occur.Peak power shortage situation frequently goes out The development of the normal life quality and local economy industry of local resident is now had a strong impact on, in order to solve this peak-valley difference, country Have to furnish a huge amount of money for build the progress peak regulation such as variable load plant, hydroenergy storage station, cost is higher and easily causes the wasting of resources.
With a series of issue of files of State Council and the Committee of Development and Reform, electric system reform sequence lasts go deep into, sale of electricity The characteristics of business is also constantly open to the society, and electrovalence policy will be presented flexibility, personalization, become more meticulous.Guangdong Province is preferentially carried out Power market transaction rule pilot, be made up of capacity price of electricity and electricity price two are released for the larger power user in industry and commerce Portion's electricity price, and introduce penalty mechanism processing load peak user in excess of the quota.The introducing of this penalty mechanism has promoted to use Family optimizes to itself electric energy use, to increase the benefit.
In fact, using the U.S. as in the Foreign Electricity Markets of representative, in order to alleviate the electricity shortage situation of peak period, Generally entitled " Demand Charge " demand charging mode is used for industry and commerce user：The actual electricity bill of user by Two parts are formed, and a part is charged by actual power consumption, and unit is kWh, and another part is by the maximum in a period of time Performance number is charged, and unit is kW.Within the charge cycle, if user's peak-to-average power increases 1kW, its caused demand charge Can be suitable with the electricity charge of multi-purpose nearly Baidu's electricity.The demand charging mode of North America electricity market is charged with Guangdong Electric Power market two -part system Pattern is very similar, such as the power demand time series of a user is (p1,p2,…,pn), then the user is in the charge The electricity charge in cycle can be expressed as c1·∑pi+c2·max{pi, wherein c1To be charged for the electricity charge of actual power consumption, c2 To be charged for the demand of demand power consumption.Different according to electricity charge single structure, in general, expense caused by peak value can account for electricity 20%~30% taken, for some large commercial users or specific industrial user, regular meeting is compared in the electricity charge shared by demand charge Increase, certain customers are even up to 50%.Therefore, the peak value of user power utilization is cut down by certain means, can directly be brought Income in expense.
In order to solve the above problems, energy storage technology is introduced in power system.User side energy storage is a kind of important storage Energy technology, is different from Generation Side energy storage and defeated, distribution level energy storage, and monomer project is much smaller, closer to conventional power user.Should Kind energy storage mode can effectively realize dsm, have and eliminate peak-valley difference, smooth load, promote the utilization of new energy, The functions such as power supply cost are reduced, there is extensive researching value.
In practice at this stage, researcher realizes above-mentioned function by distributed energy management system, that is, is System is mainly made up of local side energy management system and remote server, the user that remote server scheduled store local side is sent Electric load service condition, high-precision load forecast functions are realized and by prediction result by designed Load Forecast Algorithm Timing is sent to local side；Local side combination load prediction situation calculates preferable peak-peak, and controls energy-storage system discharge and recharge Ensure to realize optimization aim in the case of actual load, by the way that battery is divided into two parts, basis optimization percentage of batteries is used for pair Perfect forecast load optimizes, and spare part battery is then used to make up random error caused by load prediction is forbidden.
Research process in terms of energy-storage system peak clipping optimizes and minimizes power cost, energy storage system capacity are often Determine a key factor of optimum results.And because energy-storage system is costly, being often depending on for actual optimum capacity is each Kind factor, such as energy-storage system cost, power network charging mechanism, load curve, the even limitation at power network end, Load Forecast Algorithm standard True property etc., how with reference to practical application scene the key that the energy-storage system of suitable size is often such optimization problem is selected, Maximize net benefits.
The content of the invention
In view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to provide a kind of consideration capacity price of electricity and load prediction error Energy storage system capacity collocation method, this method is based on distributed energy management system, local energy-storage system battery is divided into two Part：For to predicting that load optimizes the Optimized Operation part of scheduling and for caused by load prediction error a few days ago Uncertain load optimizes the emergent part of compensation, by introducing capacity price of electricity fee system, energy-storage system economy mould Type and optimal discharge and recharge scheduling strategy, to minimize the overall average daily cost of unique user as optimization aim, carry out energy-storage system most Excellent capacity configuration.Particularly, this method proposes a kind of combination traversal iteration theory and the optimized algorithm of dual-stage optimum theory, adopts Two benches battery capacity Optimization Solution is described respectively with MILP model.
The purpose of the present invention is achieved through the following technical solutions：One kind considers capacity price of electricity and load prediction error Energy storage system capacity collocation method, comprise the following steps：
(1) the history power load of user is obtained, by obtaining going through of being stored in distributed energy Management System Data storehouse History data, load is divided into by three kinds of exemplary operation day, atypia working day and day off situations using the method for K mean cluster, Retain the exemplary operation daily load data with actual optimization meaning；From the average value of electric load hourly, i.e., 1 day 24 Individual data point is analyzed；
(2) obtain the historical forecast load of user, by improving support vector regression Load Forecast Algorithm, add temperature, Weather factor of influence, based on user's history design data training set, adjusting parameter, training pattern, obtain future anticipation load；
(3) mathematical modeling is carried out for capacity price of electricity fee system, the actual electricity charge are made up of two parts：A part according to Electricity consumption total amount in family is charged, and another part is charged according to the peak-peak of user power utilization power in certain charge cycle, full Sufficient below equation：
EB=Cpeak*Ptarget+CE*Etotal
Wherein, EB is user's total electricity bill, CpeakFor the demand expense collected for peak-peak, PtargetFor in a few days maximum Peak value, CEThe electricity charge often spent by user using 1kWh electricity, EtotalFor in a few days electricity consumption total amount；
(4) operated because energy-storage system peak clipping optimizes for odd-numbered day load, thus need calculating energy-storage system daily into This, average daily cost CcdMeet below equation：
Wherein, CcyRepresent the average annual cost of energy-storage system, CrefEnergy-storage system battery capacity is represented, OM represents energy-storage system year Equal maintenance cost, FC represent often to install the fixed cost needed for 1kWh batteries, CoRepresent that the attached of a whole set of energy storage device needs is installed Addition sheet, i represent energy-storage system rebating rate, and l represents energy-storage system design life；
(5) by energy-storage system battery capacity CrefIt is divided into Cref1、Cref2Two parts, wherein Cref1To be performed for prediction load The battery capacity of Optimized Operation function, Cref2For the emergent percentage of batteries compensated for true load with prediction load deviation Capacity；
(6) the average daily cost of unique user is minimized, average daily cost includes user's odd-numbered day electricity cost and energy-storage system day Equal cost, objective function fc, its expression formula is as follows：
fc=Cpeak*Ptarget+CE*Etotal+Ccd1*Cref1+Ccd2*Cref2
Wherein Ccd1, Ccd2For the respective average daily cost of two parts battery；
Object function fcErgodic theory is solved with the optimized algorithm that dual-stage optimum theory is combined by a kind of, Step is implemented to assume P in each dual-stage optimization processtargetFor a constant, dual-stage optimization is carried out respectively successively Solve Cref1、Cref2, result that the first stage optimizes to obtain is input in second stage optimization as constant parameter and solves Cref2, Whole optimization process is in the reasonable scope by certain step-length traversal Ptarget, it is average daily with energy-storage system that the average daily electricity charge are calculated respectively Cost, traversal can obtain one group of best solution of total economic benefit after terminating；
(7) first stage optimization is carried out：Assuming that load prediction entirely accurate, for energy-storage system Optimized Operation percentage of batteries Charge and discharge process establishes MILP model, and structure is limited according to battery charging and discharging power limit and battery state of charge Constraints is built, constraints expression formula is as follows：
minfstageI=minCcd1*Cref1
Wherein：fstageIThe optimization aim of first stage is represented,Represent battery charge power,Represent Energy-storage system inverter maximum charge power,Represent battery discharge power,Represent that inverter is maximum Discharge power.uone(t) be 0-1 variables, to control under energy-storage system synchronization can only charge or discharge, wherein 1 is fill Electric situation, 0 is discharge scenario.Represent the actual charge capacity of the t stage battery, ηchargeRepresent battery charging Efficiency, Δ t represent time interval, ηdischargeRepresent cell discharge efficiency, SOCminRepresent battery minimum active volume, SOCmax Represent battery maximum available, SoCiniFor initial time battery charge capacity,For the energy-storage system of initial time Charge capacity,For the charge capacity of finish time in odd-numbered day energy-storage system,Needed for power network end actual load Ask, Pforecast(t) it is prediction load；
(8) second stage optimization is carried out：Meet an urgent need percentage of batteries capacity for energy-storage system, limited according to battery charging and discharging power System and battery state of charge limitation structure constraints, the stage battery capacity is a variable quantity Cref2(t),Cref2(1) it is equivalent In Cref2, and considering battery linear impairments in optimizing in future, related constraints is characterized as below：
minfstageII=minCcd2*Cref2
Cref2(t)-Cref2(t-Δt)≤0
Wherein：fstageIIThe optimization aim of second stage is represented,The stage is represented respectively Battery charging and discharging power.uI(t) it is the first stage battery charging and discharging behavior variable of definition, for coordinating two benches inverter work It is consistent, utwo(t) it is 0-1 variables, control energy-storage system is within the unified moment, Preal(t) it is actual load, The actual charge capacity of the stage battery is represented,For the energy-storage system state of charge of initial time,For knot The state of charge of beam moment energy-storage system, ZcyTo consider the linear fissipation factor of battery.
Further, the K mean cluster method employed in the step 1 is total to the daily electric load in historical data Measure and load x maximums are as clustering object per hour, with K points (K=3) in space for cluster centre ciClustered, to most Close to their object categorization；By the method for iteration, the value of each cluster centre is gradually updated, until obtaining best cluster knot Fruit, its result meet equation below：
Further, realize that high-precision load is pre- using improvement support vector regression Load Forecast Algorithm in the step 2 Survey；Fitting result is obtained by solving-optimizing problem, in the training process, as long as ensureing estimate and actual value y certain Within scope ε, acceptable is taken as, the optimization problem expression formula is as follows：
This method passes through Nonlinear MappingThe sample of lower dimensional space is mapped to higher dimensional space F, it is then empty in higher-dimension Between in returned by Linear Estimation, wherein w represent weight vectors, wTFor its transposed vector, b represents linear threshold parameter, Relevant parameter is obtained by introducing training set.
Further, support vector regression Load Forecast Algorithm is improved employed in the step 2 to be built by historical data Training set trains to obtain model parameter, makes (yi+1,xi) be least square method supporting vector machine a training sample, wherein yi+1For The user is in the power load charge values at i+1 moment, xiFor input vector, xi=[yi,…yi-8,yi-24+1,yi-7*24+1,Ti+1,Wi+1], yi,…yi-8The power load charge values at i moment to i-8 moment, y are represented respectivelyi-24+1,yi-7*24+1Respectively proxima luce (prox. luc) and the last week The load actual value at moment to be predicted, Ti+1For the temperature conditions of the weather forecast at i+1 moment, unit is degree Celsius Wi+1For The weather condition of the weather forecast at i+1 moment, Wi+1Value have four kinds of situations, be shown below, correspond to the i+1 moment respectively Four kinds of common weather conditions, i.e., fine, cloudy, rain, snow：
Wi+1=[1,0,0,0]；[0,1,0,0]；[0,0,1,0]；[0,0,0,1]
Compared with prior art, the advantage of the invention is that：(1) the distributed energy management system of actual motion is considered, Consider load prediction inaccuracy factor in practical application, energy-storage system is divided into basic excellent for what is optimized to ideal load Change part and the emergent part optimized for the randomness load to caused by load prediction error；(2) this method is innovative Ground proposes a kind of optimized algorithm for being combined ergodic algorithm with dual-stage optimized algorithm, considers electricity charge system, energy storage system Unite economical model and optimal discharge and recharge scheduling strategy factor, two benches are described using MILP model respectively Battery capacity optimization process；(3) a large amount of history Power system load datas are based on, by improving support vector regression Load Forecast Algorithm High-precision forecast load is generated, two groups of load datas are respectively used to solve respectively in different phase optimization.For a user, carry The high science and accuracy of energy storage system capacity collocation method, has important scientific meaning to the research promotion of energy-storage system And application value.
Brief description of the drawings
Fig. 1 is the system framework figure of the distributed energy management system of actual motion；
Fig. 2 is a kind of combination traversal iteration theory and the optimized algorithm flow chart of dual-stage optimum theory；
Fig. 3 is Power Load Forecasting Algorithm flow chart；
Fig. 4 is that various Load Forecast Algorithm ratios of precision are relatively schemed；
Fig. 5 is dual-stage optimum results figure.
Embodiment
The present invention is described in further detail with specific embodiment below in conjunction with the accompanying drawings.
A kind of energy storage system capacity collocation method for considering capacity price of electricity and load prediction error proposed by the present invention, including Following steps：
(1) the history power load of user is obtained, by obtaining going through of being stored in distributed energy Management System Data storehouse History data, load is divided into by three kinds of exemplary operation day, atypia working day and day off situations using the method for K mean cluster, Retain the exemplary operation daily load data with actual optimization meaning；Wherein to the daily electric load total amount in historical data and Load x maximums are as clustering object per hour, with K points (K=3) in space for cluster centre ciClustered, near Their object categorization；By the method for iteration, the value of each cluster centre is gradually updated, until best cluster result is obtained, Its result meets equation below：
From the average value of electric load hourly, i.e. 1 day 24 data point are analyzed；
(2) the historical forecast load of user is obtained, by improving support vector regression Load Forecast Algorithm, obtains high accuracy Load prediction results.Support vector regression Load Forecast Algorithm is improved to be trained to obtain model ginseng by historical data structure training set Number, makes (yi+1,xi) be least square method supporting vector machine a training sample, wherein yi+1For the user the i+1 moment electricity Power load value, xiFor input vector, xi=[yi,…yi-8,yi-24+1,yi-7*24+1,Ti+1,Wi+1], yi,…yi-8When representing i respectively It is carved into the power load charge values at i-8 moment, yi-24+1,yi-7*24+1The load at the respectively moment to be predicted of proxima luce (prox. luc) and the last week is true Real value, Ti+1For the temperature conditions of the weather forecast at i+1 moment, unit is degree Celsius Wi+1For the weather forecast at i+1 moment Weather condition, Wi+1Value have four kinds of situations, be shown below, correspond to four kinds of common weather conditions at i+1 moment respectively, I.e. fine, cloudy, rain, snow：
Wi+1=[1,0,0,0]；[0,1,0,0]；[0,0,1,0]；[0,0,0,1]
Fitting result is obtained by solving-optimizing problem, in the training process, as long as ensureing that estimate and actual value y exist Within certain limit ε, acceptable is taken as, the optimization problem expression formula is as follows：
This method passes through Nonlinear MappingThe sample of lower dimensional space is mapped to higher dimensional space F, it is then empty in higher-dimension Between in returned by Linear Estimation, wherein w represent weight vectors, wTFor its transposed vector, b represents linear threshold parameter, Relevant parameter is obtained by introducing training set.Support vector regression prediction algorithm precision of prediction is improved to return higher than multiple linear Return, the conventional Load Forecast Algorithm such as BP neural network；
(3) mathematical modeling is carried out for capacity price of electricity fee system, the actual electricity charge are made up of two parts：A part according to Electricity consumption total amount in family is charged, and another part is charged according to the peak-peak of user power utilization power in certain charge cycle, full Sufficient below equation：
EB=Cpeak*Ptarget+CE*Etotal
Wherein, EB is user's total electricity bill, CpeakFor the demand expense collected for peak-peak, PtargetFor in a few days maximum Peak value, CEThe electricity charge often spent by user using 1kWh electricity, EtotalFor in a few days electricity consumption total amount；
(4) operated because energy-storage system peak clipping optimizes for odd-numbered day load, thus need calculating energy-storage system daily into This, average daily cost CcdMeet below equation：
Wherein, CcyRepresent the average annual cost of energy-storage system, CrefEnergy-storage system battery capacity is represented, OM represents energy-storage system year Equal maintenance cost, FC represent often to install the fixed cost needed for 1kWh batteries, CoRepresent that the attached of a whole set of energy storage device needs is installed Addition sheet, i represent energy-storage system rebating rate, and l represents energy-storage system design life；
(5) by energy-storage system battery capacity CrefIt is divided into Cref1、Cref2Two parts, wherein Cref1To be performed for prediction load The battery capacity of Optimized Operation function, Cref2For the emergent percentage of batteries compensated for true load with prediction load deviation Capacity；
(6) the average daily cost of unique user is minimized, average daily cost includes user's odd-numbered day electricity cost and energy-storage system day Equal cost, objective function fc, its expression formula is as follows：
fc=Cpeak*Ptarget+CE*Etotal+Ccd1*Cref1+Ccd2*Cref2
Wherein Ccd1, Ccd2For the respective average daily cost of two parts battery；
Object function fcErgodic theory is solved with the optimized algorithm that dual-stage optimum theory is combined by a kind of, Step is implemented to assume P in each dual-stage optimization processtargetFor a constant, dual-stage optimization is carried out respectively successively Solve Cref1、Cref2, result that the first stage optimizes to obtain is input in second stage optimization as constant parameter and solves Cref2, Whole optimization process is in the reasonable scope by certain step-length traversal Ptarget, it is average daily with energy-storage system that the average daily electricity charge are calculated respectively Cost, traversal can obtain one group of best solution of total economic benefit after terminating；
(7) first stage optimization is carried out：Assuming that load prediction entirely accurate, for energy-storage system Optimized Operation percentage of batteries Charge and discharge process establishes MILP model, and structure is limited according to battery charging and discharging power limit and battery state of charge Constraints is built, constraints expression formula is as follows：
minfstageI=minCcd1*Cref1
Wherein：fstageIThe optimization aim of first stage is represented,Represent battery charge power,Represent Energy-storage system inverter maximum charge power,Represent battery discharge power,Represent that inverter is maximum Discharge power.uone(t) be 0-1 variables, to control under energy-storage system synchronization can only charge or discharge, wherein 1 is fill Electric situation, 0 is discharge scenario.Represent the actual charge capacity of the t stage battery, ηchargeRepresent battery charging Efficiency, Δ t represent time interval, ηdischargeRepresent cell discharge efficiency, SOCminRepresent battery minimum active volume, SOCmax Represent battery maximum available, SoCiniFor initial time battery charge capacity,For the energy-storage system of initial time Charge capacity,For the charge capacity of finish time in odd-numbered day energy-storage system,Needed for power network end actual load Ask, Pforecast(t) it is prediction load；
(8) second stage optimization is carried out：The stage is uncertain caused by mainly for eliminating prediction error, for energy storage System emergency percentage of batteries capacity is according to the condition structure constraint bar such as battery charging and discharging power limit and battery state of charge limitation Part, mathematical modeling is carried out, it is necessary to which it is specifically intended that one energy-storage system of two parts battery sharing, first stage battery fill Electric discharge behavior can influence the battery behavior of second stage.Need additionally to supplement be this percentage of batteries in a few days optimizing can be due to The frequent discharge and recharge of load prediction error, odd-numbered day charge and discharge cycles number are two to three times of first stage battery, and battery loss is fast It is fast, serious, it is considered herein that this percentage of batteries is a variable quantity Cref2(t),Cref2(1) it is equal to Cref2, and consider in future Battery linear impairments in optimization, related constraints are characterized as below：
minfstageII=minCcd2*Cref2
Cref2(t)-Cref2(t-Δt)≤0
Wherein：fstageIIThe optimization aim of second stage is represented,The stage is represented respectively Battery charging and discharging power.uI(t) it is the first stage battery charging and discharging behavior variable of definition, for coordinating two benches inverter work It is consistent, utwo(t) it is 0-1 variables, control energy-storage system is within the unified moment, Preal(t) it is actual load, The actual charge capacity of the stage battery is represented,For the energy-storage system state of charge of initial time,For knot The state of charge of beam moment energy-storage system, ZcyTo consider the linear fissipation factor of battery.
Embodiment
Distributed energy management system (its system framework is as shown in Figure 1) of the invention based on actual motion, distributed energy Management system is the representative solution of the Demand-side management of power load at this stage：Mainly include three parts：Remote service Device, indigenous energy management system and energy storage system；
Its remote server is responsible for storing history electric load information and performs load forecast functions；Indigenous energy management System at regular intervals uploads local user's electricity consumption situation and obtains the compound information of forecasting of server, true by optimal discharge and recharge scheduling strategy Determine discharge and recharge behavior, control energy storage system (battery) carries out reasonable discharge and recharge；
The present invention carries out load forecast functions first, using improvement Support vector regression algorithm (load prediction in invention Algorithm flow chart as shown in figure 3, prediction result as shown in figure 4, the improvement SVMs that Fig. 4 results display present invention designs returns Return Load Forecast Algorithm precision higher than multiple linear regression and BP neural network Load Forecast Algorithm), by remote server A large amount of history Power system load datas networking of storage obtains future time instance weather temperature data and meteorological data, is trained by building Collection calculates support vector regression model, performs load forecast functions acquisition load prediction data by the model and stores；Filling Separately win after taking historical data and load prediction data, energy storage system capacity configuration is carried out by set algorithm；
The present invention travels through iteration theory and optimized algorithm (the algorithm stream of dual-stage optimum theory by a kind of combination of design Journey figure such as Fig. 2) specific aim calculating is carried out to two parts battery：Step is implemented to assume in each dual-stage optimization process PtargetFor a constant, dual-stage optimization is carried out respectively and solves C successivelyref1、Cref2, the first stage optimizes obtained result conduct Constant parameter is input in second stage optimization and solves Cref2, according to pressing certain step in the reasonable scope in whole optimization process Long traversal iteration Ptarget, the average daily electricity charge and the average daily cost of energy-storage system are recorded respectively, and traversal can obtain total economic benefit after terminating The solution of best solution, as this method.(the dual-stage optimized algorithm of result display design is well as shown in Figure 5 for optimum results Perform optimization function), wherein it can be found that the P that first stage and second stage all maintain liketarget, it is calculated simultaneously Optimized Operation percentage of batteries capacity 56kWh, meet an urgent need percentage of batteries capacity 11.3kWh, by a large amount of to different exemplary operation days Calculate, it is a discovery of the invention that the emergent percentage of batteries of about 15%~20% Optimized Operation part size makes up load prediction enough The uncertain influence for optimizing function to distributed energy management system.
The preferred embodiment of the invention is the foregoing is only, is not intended to limit the invention creation, it is all at this All any modification, equivalent and improvement made within the spirit and principle of innovation and creation etc., should be included in the invention Protection domain within.

Claims (4)

  1. A kind of 1. energy storage system capacity collocation method for considering capacity price of electricity and load prediction error, it is characterised in that including with Lower step：
    (1) the history power load of user is obtained, by obtaining the history number stored in distributed energy Management System Data storehouse According to, load is divided into by three kinds of exemplary operation day, atypia working day and day off situations using the method for K mean cluster, retain Exemplary operation daily load data with actual optimization meaning；From the average value of electric load hourly, i.e. 1 day 24 number Analyzed at strong point；
    (2) the historical forecast load of user is obtained, by improving support vector regression Load Forecast Algorithm, adds temperature, weather Factor of influence, based on user's history design data training set, adjusting parameter, training pattern, obtain future anticipation load；
    (3) mathematical modeling is carried out for capacity price of electricity fee system, the actual electricity charge are made up of two parts：A part is used according to user Electric total amount is charged, and another part is charged according to the peak-peak of user power utilization power in certain charge cycle, meet with Lower formula：
    EB=Cpeak*Ptarget+CE*Etotal
    Wherein, EB is user's total electricity bill, CpeakFor the demand expense collected for peak-peak, PtargetFor in a few days peak-peak, CEThe electricity charge often spent by user using 1kWh electricity, EtotalFor in a few days electricity consumption total amount；
    (4) the average daily cost of energy-storage system, average daily cost C are calculatedcdMeet below equation：
    <mrow> <msub> <mi>C</mi> <mrow> <mi>c</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <mi>i</mi> <msup> <mrow> <mo>(</mo> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>l</mi> </msup> </mrow> <mrow> <msup> <mrow> <mo>(</mo> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>l</mi> </msup> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <mo>*</mo> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> </mrow> </msub> <mo>*</mo> <mi>F</mi> <mi>C</mi> <mo>+</mo> <msub> <mi>C</mi> <mi>O</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> </mrow> </msub> <mo>*</mo> <mi>O</mi> <mi>M</mi> </mrow>
    <mrow> <msub> <mi>C</mi> <mrow> <mi>c</mi> <mi>d</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mn>365</mn> </mfrac> <mo>*</mo> <msub> <mi>C</mi> <mrow> <mi>c</mi> <mi>y</mi> </mrow> </msub> </mrow>
    Wherein, CcyRepresent the average annual cost of energy-storage system, CrefEnergy-storage system battery capacity is represented, OM represents that energy-storage system is tieed up every year Cost is protected, FC represents often to install the fixed cost needed for 1kWh batteries, CoRepresent that adding into for a whole set of energy storage device needs is installed This, i represents energy-storage system rebating rate, and l represents energy-storage system design life；
    (5) by energy-storage system battery capacity CrefIt is divided into Cref1、Cref2Two parts, wherein Cref1To perform optimization for prediction load The battery capacity of scheduling feature, Cref2Emergent percentage of batteries to be compensated for true load with prediction load deviation holds Amount；
    (6) minimize the average daily cost of unique user, average daily cost include user's odd-numbered day electricity cost and energy-storage system daily into This, objective function fc, its expression formula is as follows：
    fc=Cpeak*Ptarget+CE*Etotal+Ccd1*Cref1+Ccd2*Cref2
    Wherein Ccd1, Ccd2For the respective average daily cost of two parts battery；
    Object function fcErgodic theory is solved with the optimized algorithm that dual-stage optimum theory is combined by a kind of, specifically Step is realized to assume P in each dual-stage optimization processtargetFor a constant, dual-stage optimization is carried out respectively and is solved successively Cref1、Cref2, result that the first stage optimizes to obtain is input in second stage optimization as constant parameter and solves Cref2, entirely Optimization process is in the reasonable scope by certain step-length traversal Ptarget, the average daily electricity charge and the average daily cost of energy-storage system are calculated respectively, Traversal can obtain one group of best solution of total economic benefit after terminating；
    (7) first stage optimization is carried out：Assuming that load prediction entirely accurate, for energy-storage system Optimized Operation percentage of batteries charge and discharge Electric process establishes MILP model, is built about according to battery charging and discharging power limit and battery state of charge limitation Beam condition, constraints expression formula are as follows：
    min fstageI=min Ccd1*Cref1
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mi>max</mi> </msubsup> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <mn>0</mn> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msup> <mi>u</mi> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>*</mo> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mi>max</mi> </msubsup> </mrow>
    <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msup> <mi>u</mi> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> <mo>*</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow>
    <mrow> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mi>&amp;Delta;</mi> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <msub> <mi>&amp;eta;</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> </msub> </mfrac> <mo>*</mo> <mi>&amp;Delta;</mi> <mi>t</mi> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>*</mo> <msub> <mi>&amp;eta;</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> </msub> <mo>*</mo> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow>
    <mrow> <mn>0</mn> <mo>&lt;</mo> <msub> <mi>SOC</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>*</mo> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mn>1</mn> </mrow> </msub> <mo>&amp;le;</mo> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>SOC</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>*</mo> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mn>1</mn> </mrow> </msub> </mrow>
    <mrow> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>SOC</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>i</mi> </mrow> </msub> <mo>*</mo> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mn>24</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>P</mi> <mrow> <mi>f</mi> <mi>o</mi> <mi>r</mi> <mi>e</mi> <mi>c</mi> <mi>a</mi> <mi>s</mi> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <msub> <mi>&amp;eta;</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> </msub> </mfrac> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>*</mo> <msub> <mi>&amp;eta;</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> </msub> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>P</mi> <mrow> <mi>t</mi> <mi>arg</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> </mrow>
    Wherein：fstageIThe optimization aim of first stage is represented,Represent battery charge power,Represent energy storage System inverter maximum charge power,Represent battery discharge power,Represent the maximum electric discharge of inverter Power；uone(t) be 0-1 variables, to control under energy-storage system synchronization can only charge or discharge, wherein 1 is charging feelings Condition, 0 is discharge scenario；Represent the actual charge capacity of the t stage battery, ηchargeBattery charge efficiency is represented, Δ t represents time interval, ηdischargeRepresent cell discharge efficiency, SOCminRepresent battery minimum active volume, SOCmaxRepresent electricity Pond maximum available, SoCiniFor initial time battery charge capacity,Hold for the energy-storage system electric charge of initial time Amount,For the charge capacity of finish time in odd-numbered day energy-storage system,For power network end actual load demand, Pforecast(t) it is prediction load；
    (8) second stage optimization is carried out：Met an urgent need percentage of batteries capacity for energy-storage system, according to battery charging and discharging power limit and Battery state of charge limitation structure constraints, the stage battery capacity is a variable quantity Cref2(t), Cref2(1) it is equal to Cref2, and considering battery linear impairments in optimizing in future, related constraints is characterized as below：
    min fstageII=min Ccd2*Cref2
    <mrow> <msup> <mi>u</mi> <mi>I</mi> </msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mi>i</mi> <mi>f</mi> <mi> </mi> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&gt;</mo> <mn>0</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mi>f</mi> <mi> </mi> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <mn>0</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mi>e</mi> <mi>l</mi> <mi>s</mi> <mi>e</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mi>max</mi> </msubsup> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <mn>0</mn> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msup> <mi>u</mi> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>*</mo> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mi>max</mi> </msubsup> <mo>&amp;le;</mo> <mrow> <mo>(</mo> <msup> <mi>u</mi> <mi>I</mi> </msup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>*</mo> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mi>max</mi> </msubsup> </mrow>
    <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msup> <mi>u</mi> <mi>I</mi> </msup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> <mo>*</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msup> <mi>u</mi> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> <mo>)</mo> <mo>*</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>P</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <msub> <mi>&amp;eta;</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> </msub> </mfrac> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>*</mo> <msub> <mi>&amp;eta;</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> </msub> <mo>+</mo> <mfrac> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <msub> <mi>&amp;eta;</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> </msub> </mfrac> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>*</mo> <msub> <mi>&amp;eta;</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> </msub> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>P</mi> <mrow> <mi>t</mi> <mi>arg</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> </mrow> 2
    <mrow> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mi>&amp;Delta;</mi> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <msub> <mi>&amp;eta;</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> </msub> </mfrac> <mo>*</mo> <mi>&amp;Delta;</mi> <mi>t</mi> <mo>+</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>*</mo> <msub> <mi>&amp;eta;</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>c</mi> <mi>h</mi> <mi>arg</mi> <mi>e</mi> </mrow> </msub> <mo>*</mo> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow>
    <mrow> <mn>0</mn> <mo>&lt;</mo> <msub> <mi>SOC</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>*</mo> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>SOC</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>*</mo> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>SOC</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>i</mi> </mrow> </msub> <mo>*</mo> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mn>2</mn> </mrow> </msub> <mo>=</mo> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mn>24</mn> <mo>)</mo> </mrow> </mrow>
    Cref2(t)-Cref2(t-Δt)≤0
    <mrow> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mi>&amp;Delta;</mi> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>Z</mi> <mrow> <mi>c</mi> <mi>y</mi> </mrow> </msub> <mo>*</mo> <mrow> <mo>(</mo> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow> <mo>)</mo> <mo>-</mo> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>t</mi> <mi>w</mi> <mi>o</mi> </mrow> </msubsup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <mn>0</mn> </mrow>
    Wherein：fstageIIThe optimization aim of second stage is represented,The stage battery is represented respectively Charge-discharge electric power；uI(t) it is the first stage battery charging and discharging behavior variable of definition, is protected for coordinating the work of two benches inverter Hold consistent, utwo(t) it is 0-1 variables, control energy-storage system is within the unified moment, Preal(t) it is actual load,Represent The actual charge capacity of the stage battery,For the energy-storage system state of charge of initial time,For at the end of Carve the state of charge of energy-storage system, ZcyTo consider the linear fissipation factor of battery.
  2. 2. according to the method for claim 1, it is characterised in that the K mean cluster method employed in the step 1 is to going through Daily electric load total amount in history data and per hour load x maximums are as clustering object, using K points in space as in cluster Heart ciClustered, to the object categorization near them；By the method for iteration, the value of each cluster centre is gradually updated, directly To best cluster result is obtained, its result meets equation below：
    <mrow> <mi>min</mi> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>x</mi> <mo>&amp;Element;</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> </mrow> </munder> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>t</mi> <msup> <mrow> <mo>(</mo> <msub> <mi>c</mi> <mi>i</mi> </msub> <mo>,</mo> <mi>x</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>.</mo> </mrow>
  3. 3. according to the method for claim 1, it is characterised in that using improvement support vector regression load in the step 2 Prediction algorithm realizes high-precision load prediction；Fitting result is obtained by solving-optimizing problem, in the training process, as long as protecting Estimate and actual value y are demonstrate,proved within a scope ε, is taken as acceptable, the optimization problem expression formula is as follows：
    <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mo>|</mo> <mo>|</mo> <mi>w</mi> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow>
    This method passes through Nonlinear MappingThe sample of lower dimensional space is mapped to higher dimensional space F, then in higher dimensional space Returned by Linear Estimation, wherein w represents weight vectors, wTFor its transposed vector, b represents linear threshold parameter, related Parameter is obtained by introducing training set.
  4. 4. according to the method for claim 1, it is characterised in that support vector regression is improved employed in the step 2 and is born Lotus prediction algorithm is trained to obtain model parameter by historical data structure training set, makes (yi+1,xi) it is least square method supporting vector machine A training sample, wherein yi+1It is the user in the power load charge values at i+1 moment, xiFor input vector xi=[yi, ...yi-8,yi-24+1,yi-7*24+1,Ti+1,Wi+1], yi,...yi-8The power load charge values at i moment to i-8 moment are represented respectively, yi-24+1,yi-7*24+1The respectively load actual value at the moment to be predicted of proxima luce (prox. luc) and the last week, Ti+1For the weather at i+1 moment The temperature conditions of forecast, unit are degree Celsius Wi+1For the weather condition of the weather forecast at i+1 moment, Wi+1Value have four kinds Situation, it is shown below, corresponds to four kinds of common weather conditions at i+1 moment, i.e., fine, cloudy, rain, snow respectively：
    Wi+1=[1,0,0,0]；[0,1,0,0]；[0,0,1,0]；[0,0,0,1].
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