CN107862466A - The source lotus complementary Benefit Evaluation Method spanning space-time of consideration system bilateral randomness - Google Patents

Abstract

A kind of source lotus complementary Benefit Evaluation Method spanning space-time of consideration system bilateral randomness, it is characterized in that, including：Build different cleaning power supply typical case output property data base spanning space-time；The load forecasting model based on Data Mining technology is established, and considers that polynary influence factor predicts following different regions part throttle characteristics；Consider different regions natural conditions, regional disparity, energy structure difference external influence factors, build the trans-regional source lotus space-time complementary characteristic evaluation system of the statistical nature based on electric power big data.Take into full account that complementation of the regenerative resource with load in wide scope, spanning space-time is distributed rationally out of global range, can to it is transnational, trans-regional, across continent, consider broad sense, multi-source, polymorphic, isomery data difference, characteristic statisticses based on electric power big data, trans-regional source lotus space-time complementation benefit is assessed in all directions from complementary, lotus lotus complementation in a steady stream and complementary three dimensions of source lotus, is widely used in the Rationality Assessment of complementary benefit spanning space-time to source lotus.

Description

The source lotus complementary Benefit Evaluation Method spanning space-time of consideration system bilateral randomness

Technical field

The present invention relates to the technical field of generating, power transformation or transmission of electricity, more particularly to a kind of consideration system bilateral randomness Source lotus complementary Benefit Evaluation Method spanning space-time.

Background technology

The factors such as climate change, environmental pollution, energy security influence, and global energy is to green, low-carbon, intelligence Direction accelerates transition.Large-scale developed and utilized using wind energy, solar power generation as the clean energy resource of representative, the energy is promoted in various countries The role to become more and more important is play in transformation process.Consider that honourable resource distribution differs greatly in global range, is typically remote from negative Lotus center, remote Large Copacity electrical grid transmission can be needed by generating electricity, therefore global energy Internet Strategy concept is arisen at the historic moment.Entirely Ball energy internet is leading using clean energy resource, and using extra-high voltage grid as bulk transmission grid, each continent power network in various countries interconnects extensively, is formed Global energy most optimum distribution of resources, it is the grand strategy conception of global energy transition, has obtained extensive common recognition in the world, at present Entry strategies implementation phase.

Power system be one require hair, it is defeated, match somebody with somebody, electricity consumption loopful section simultaneously completion time-varying system, the original of system balancing It is then the follow load change of regulation normal power supplies output, to keep dynamic equilibrium.But can as representative using wind-powered electricity generation, photovoltaic generation The renewable sources of energy have randomness, intermittence and fluctuation feature, on machine unit characteristic, generation mode with conventional electric power generation unit difference It is very big；On the other hand, the random discharge and recharge using electric automobile as representative and demand response also exacerbate the random of load side significantly Property and prediction difficulty.Therefore, increase with the scale of source lotus both sides dual random, fluctuation, how to ensure power train It is as much as possible under the conditions of system safe and stable operation to receive generation of electricity by new energy and meet load random demand, i.e., in bilateral randomness Under the influence of maintain system real-time dynamic power balance to thirst for solving always as those skilled in the art, but not yet dissatisfied problem.

The space-time complementation of different energy sources resource and load can reduce the randomness and fluctuation of power network both sides, improve system Stability and comprehensive benefit.It is how square using new energy and traditional energy generating bundling etc. using the complementary characteristic of wind-powered electricity generation, photovoltaic Its smooth generating output-power fluctuation of formula, and use dsm peak load shifting to reduce using the complementary characteristic of load To the adverse effect of power system, existing numerous documents propose correlation techniques both at home and abroad, but do not account for can for these methods Complementation of the renewable sources of energy with load in wide scope, spanning space-time is distributed rationally；In addition, Interconnection Scale constantly expands, electric power, energy The multi-source of source and broad sense environment, polymorphic and isomeric data quantity will exponentially increase, it is necessary to have corresponding wide area collection, height Effect storage and rapid treating technology are supported, and Extracting Knowledge and value application from these data.Therefore, with global energy The continuous propulsion of source Internet Strategy, source lotus complementary characteristic transnational, trans-regional, even across continent need further investigation badly.

The content of the invention

In view of the above-mentioned problems, it is an object of the invention to provide a kind of scientific and reasonable, strong applicability, the good consideration system of effect The source lotus complementary Benefit Evaluation Method spanning space-time of bilateral randomness, electric power big data is combined by it with transregional complementary theory, Generation Side compiles clean energy resource generation assets characteristic of the different regions containing wind-powered electricity generation, solar power generation, with reference to all kinds of generators Group model, obtain the power producing characteristics that the corresponding clean energy resource containing local wind-powered electricity generation, solar power generation generates electricity；Compiled in load side Different regions historical load statistics, consider local population, economic development, the industrial structure, per capita energization ratio, electricity consumption Amount factor, prediction is following to include power consumption, peak load, peak-valley difference part throttle characteristics；In combination with weather, topography and geomorphology nature bar Part, regional disparity caused by grid-connected basis, technology maturity, and containing the industrial structure, the energy structure difference of power mode, base In the characteristic statisticses of electric power big data, from complementary, lotus lotus complementation in a steady stream and complementary three dimensions of source lotus, build in all directions transregional Domain source lotus space-time complementation benefit, implement to provide theoretical foundation for the landing of next step global energy Internet Strategy conception.

To achieve the above object, the present invention takes following technical scheme：A kind of source lotus of consideration system bilateral randomness across Space-time complementation Benefit Evaluation Method, it is characterized in that, comprise the step of：

1) different cleaning power supply typical case output property data base spanning space-time is built；

2) load forecasting model based on Data Mining technology is established, and considers that polynary influence factor prediction is following differently Area's part throttle characteristics；

3) consider that more external influence factors, the structures such as different regions natural conditions, regional disparity, energy structure difference are based on The trans-regional source lotus space-time complementary characteristic evaluation system of the statistical nature of electric power big data.

Structure different cleaning power supply typical case output property data base spanning space-time, is comprised the step of in the step 1)：

(Ι) chooses typical country or area, for solar energy, wind energy, water energy clean energy resource, compiles different regions wind The resource characteristicses that electricity, solar power generation, water power clean energy resource generate electricity, screen and screen the attribute for portraying clean energy resource distribution characteristics Set；

(II) it is small with reference to all kinds of generating set models, simulation according to the natural characteristic and changing rule of related energy resources When/season/year Multiple Time Scales different cleaning power supply generated output characteristic；

(III) according to the energy resources feature and historical data of different zones, i.e. countries/regions/continent, fitting is following should The typical power curve of the clean energy resource in country or continent generating and characteristic.

, it is necessary in the world in (Ι) of the step 1), according to honourable clean energy resource resource distribution general status, Select the typical country or continent for being adapted to large-scale develop and utilize, count the country or continent wind speed, Intensity of illumination stock number, specifies resource potential and technical exploitation amount.

The source lotus complementary Benefit Evaluation Method spanning space-time of described consideration system bilateral randomness, it is characterized in that, the step In rapid (II) 1), pass through Wind turbines output power model, wind power plant/group's output power model, photovoltaic array power output Wind energy and solar energy resources characteristic are converted into wind-powered electricity generation and solar power generation power producing characteristics, the calculating formula of the model by model respectively For formula (1)-formula (5)：

1. Wind turbines output power model

For Wind turbines, containing asynchronous generator, doubly fed induction generator and synchronous induction motor, Wind turbines are contributed and wind Functional relation between speed, wherein, P_rFor the rated power of wind power generating set, V_ciTo cut wind speed, V_rFor rated wind speed, V_co For cut-out wind speed,

Wind turbines power output P_WFunctional relation between wind speed v is formula (1)：

A, B, C are Wind turbines power characteristic parameter, and different Wind turbines can be slightly different, and expression formula is formula (2)：

2. wind power plant/group's output power model

Wind power plant is made up of tens even typhoon group of motors up to a hundred being installed in parallel in same place, due to the shadow of wake flow Ring, the wind speed for being located Wind turbines on the leeward will be less than being seated in the wind speed of wind upwind group of motors, referred to as wake flow Effect, it is determined that must take into consideration wake effect during Power Output for Wind Power Field, common wake effect model have Jensen models and Lissaman models, the Wind turbines in level terrain can use Jensen model analysis, the Wind turbines in complicated landform Lissaman modelings can be used, for the wind power plant of planning stage, it is assumed that the wind speed of all Wind turbines in same wind power plant Identical with wind direction, it is the wind power plant to be multiplied by a coefficient for representing wake effect with all Wind turbines gross output sums Power output,

The operating experience of wind power plant shows, can be lost by rational deployment to reduce wake effect, what wake flow caused damage Representative value is 10%, and the gross output of Wind turbines now is multiplied by into canonical system numerical value 0.9 to represent the reality output of wind power plant Power；

3. photovoltaic array output power model

The power output of photovoltaic array depends on intensity of solar radiation, array area and electricity conversion, for one Photovoltaic array with M battery component, the area and photoelectric transformation efficiency of each component are respectively A_mAnd η_m(m=1,2 ..., M), then the power output of photovoltaic array is formula (3)-formula (5)：

P_solar=IA η (3)

In formula, A and η are respectively the gross area of array and equivalent photoelectric transformation efficiency.

The source lotus complementary Benefit Evaluation Method spanning space-time of described consideration system bilateral randomness, it is characterized in that, the step In rapid 1) (III), by country variant, area or the new energy in continent historical statistical data and characteristic, based on MCMC model inversions Following different regions new energy power producing characteristics are simulated, the specific construction method of model is：

1. define stochastic variable state

The span of wind speed is wider in actual wind power plant, from 0m/s to 30m/s, under extreme case even more than 40m/s, when establishing wind speed time series models with MCMC methodology, suitable status number should be chosen according to wind speed profile scope first N, incorporated into according to the size of each time point wind speed to different states, original air speed data be converted into discrete state point, The state of stochastic variable defines the method for taking decile span, also k-means methods can be taken to be clustered to obtain；

2. structural regime transfer matrix

Wind farm wind velocity can be divided into different states, and what gustiness transfer characteristic described is exactly wind speed in different time Under yardstick, the probability nature that is shifted between different conditions in order to quantify this characteristic, introduces the concept of state-transition matrix, State-transition matrix P is N × N matrix, wherein, N is the status number divided to wind speed value scope, each member in P Plain p_ijValue represents is wind speed current time as state i, be transferred to state j probability in subsequent time, i.e.,

p_ij=P (X_t=j | X_t-1=i) (6)

Corresponding to one step state transition matrix, defining multistep state-transition matrix is：

The matrix of other each state probability values is transferred to after n unit interval comprising current time state,

The state-transition matrix that formula (6) and formula (7) define is single order battle array, that is, thinks the air speed value of subsequent time only with working as The wind speed at preceding moment is relevant, and not related with a series of wind speed before,

After the wind speed time series of certain length is obtained, (a)-(e) steps are taken to generate wind farm wind velocity state Transition probability matrix：

(a) the wind speed value scope of wind power plant is divided into N parts, per an a corresponding state, wherein, N is status number；

(b) N × N null matrix S is established, to count the number shifted between each state；

(c) first value corresponding states m, next moment value corresponding states n of wind farm wind velocity actual measurement sequence are assumed, then Matrix S corresponding element adds 1；

(d) step (c) is applied to remaining all adjacent air speed value in sequence, all correspondences is contained in obtained S State transfer number；

(e) to obtain state-transition matrix P, by each element in S divided by the element sum being expert at, i.e.,

Thus, the step state transition probability matrix P of wind farm wind velocity one has been obtained.If intentionally get multistep state transfer square Battle array, as long as the subsequent time value in step (c) is changed into lower k moment value；

3. construct cumulative probability transfer matrix

Above on state-transition matrix P basis, the building method of cumulative probability transfer matrix is formula (9)：

In formula, if P dimension is N × N, dimension be N × (N+1), the first row of accumulated probability transfer matrix is equal Be zero, since secondary series, the values of each element m rows n row be element of the m row, column number less than n corresponding in matrix P it With；

4. generate stochastic regime sequence

It is (f)-(i) using the step of accumulated probability transfer matrix and DSMC generation stochastic regime sequence：

(f) set current wind speed and be in state m；

(g) the uniform random number u on (0,1) section is generated；

(h) by u and m row elements, element line number is that current wind speed status number is compared, if meeting relation Formula, then next state be taken as n；

If (i) the stochastic regime sequence of generation has met length requirement, stop；Conversely, current state is changed into n, Return to step (g) continues；

5. construct wind farm wind velocity time series

By the time series of generation it is the status number of series of discrete, it is necessary to take certain method to be converted into wind Field gas velocity time series,

X=X_n,min+u(X_n,max-X_n,min) (10)

In formula, state n is corresponding wind speed, and X is the simulation wind speed at the moment, X_n,max、X_n,minIt is that state n is covered respectively The upper and lower bound of lid wind speed interval.

Consider that polynary influence factor predicts following different regions part throttle characteristics in the step 2), foundation is based on Data Mining The load forecasting model of technology, and consider that polynary influence factor predicts following different regions part throttle characteristics, comprise the following steps：

(IV) typical country or area are chosen, is compiled including local population, economic development, the industrial structure, energization ratio The basic datas such as example, per capita household electricity consumption, and load prediction key factor is screened based on data mining technology；

(V) different prediction scenes are built from the dimension of economic society factor, energy policy, energy technology development, establishes bag Integrated forecasting storehouse containing a variety of Individual forecast models, load is carried out using matched model for different prediction scenes Predicting Performance Characteristics, obtain including power consumption, peak load, the part throttle characteristics of peak-valley difference in following varying level year.

The source lotus complementary Benefit Evaluation Method spanning space-time of described consideration system bilateral randomness, it is characterized in that, the step In rapid (IV) 2), data mining is that the various analysis tools of utilization find relation between model and data in mass data Process, can automatically decimation pattern, association, change, abnormal and significant structure, its value be to utilize number from data Improve forecast model according to digging technology,

Load prediction key factor, which is screened, based on data mining technology mainly includes the foundation of alternative index storehouse, index Data cleansing, stationarity verification, tested to alternative index classification, co-integration relationship text, Granger causality test mistake Cheng Wei：

1. stationary test

It is the basis that whether there is stable relation between analysis time sequence that stationary test is carried out to each time series, is put down Steady time series refers to the time series with stable statistical law, i.e. the desired value of time series and variance not anaplasia at any time Change, sequence data fluctuates near desired value, explains time series in reflection and during by explanation time series relation, during non-stationary Between sequence can cause False value, i.e., " shadowing property ", shadowing property represents that explanatory variable and explained variable have preferable mathematics Be fitted characteristic, but balanced relation steady in a long-term be not present between them, when with shadowing property explanatory variable to explained variable When being modeled prediction, generally there is larger deviation in predicted value and actual value,

Using ADF methods of inspection (Augmented Dickey-Fuller Test) unit root test method, that is, pass through judgement Time series to be tested judges the stationarity of time series with the presence or absence of unit root, and algorithm is formula (11)-formula (15)：

Check-Out Time sequence is treated by regression equation to be fitted:

T=(ρ ' -1)/se (ρ ') (14)

In formula, β, η t represent intercept and trend term in time series to be tested, regression equation (11)-formula (13) respectively Selection can be selected according to the presence or absence of intercept item in time series to be tested and trend term；L represents hysteresis rank in regression equation Count, l values are generally determined by AIC minimum criterias, ε_tFor residual error item, hysteresis item is added in regression equationCan be true Protect ε_tFor white noise, in T normalized set formulas, ρ ' represents ρ least-squares estimation, and se (ρ ') represents that ρ ' unbiaseds help meter, Assuming that H₀Represent that former time series is nonstationary time series, alternative hypothesis H₁Expression time series is stationary time series, according to The T statistics values of calculating, if assuming H₀To set up, then former time series is nonstationary time series, conversely, refusal null hypothesis, i.e., Time series is stationary time series.

Found after carrying out stationary test to the index in index storehouse, electric power market demand index is the single order non-stationary time Sequence, in econometrics model, judge for the relation between nonstationary time series, can be referred to by co integration test to verify Whether there is relation steady in a long-term between mark, the premise of inspection is that each time series must be the unstable time series of same order, Therefore, after carrying out stationary test to the index time series in achievement data storehouse, reject non-flat not for same order with electricity needs Steady time series, co-integration test will be carried out with the time series of electricity needs same order non-stationary, passes through co-integration test Time series then proves relation steady in a long-term be present with electricity needs；

2. co integration test

Whole opinion of burying is assisted to think there may be relation steady in a long-term between the time series of several non-stationaries, on condition that this Several nonstationary time series belong to the unstable time series of same order, and condition is that and if only if these same order non-stationary times The linear combination of sequence is stationary time series, such as time series x_tWith y_t, belong to the time serieses of n rank non-stationaries, and this The linear combination α of two time serieses₁x_t+α₂y_tIt is that n-m ranks are singly whole, then it is assumed that time series x_tWith y_tIn the presence of steady in a long-term Relation；

The stationary test of passage time sequence, filter out and belonged to the whole non-stationary time sequence of same order list with electricity needs Row, belonging to the whole nonstationary time series of same order list with electricity needs and whether relation steady in a long-term being present with electricity needs to lead to Co-integration Theory is crossed to test；

3. Granger causality test

The causality that sequencing is referred to as between time series is fluctuated between time series, using Granger causality Examine, time series x_t, y_tBetween fluctuate sequencing performance be time series x_t, to time series y_tPrediction it is helpful, and There is stronger dependency relation in two time serieses, the principle of Granger CaFpngerusality test is：By making to assume inspection to regression analysis Test, Check-Out Time sequence x_tPast value to time series y_tPresent value can do the explanation of much degree, and co-integration relationship calculates Process is：

(j) to time variable y_tVAR (l) models are established, calculate residual sum of squares (RSS) RSS_R

(k) x is added in VAR models_tLag item x_t-jIt is shown below, and calculates RSS_UR

(l) assume：H₀:b₁=b₂=...=b_q=0, i.e. x_tHysteresis item be not belonging to y_tRecurrence in (m) calculate F examine Statistic

(n) by the critical value F in F values and the α levels of signifiance_αCompare, if F ＞ F_α, refusal hypothesis H₀,

In F examines formula, n represents the dimension of sample to be tested, and q represents time series x_tHysteresis item number, k represent treat Estimate the number of parameter, n, q and k three obey the F distributions that the free degree is q and (n-k), refuses H₀Represent, hysteresis item x_tBelong to former Regression equation, add hysteresis item x_tTime series x can be improved_tPrediction effect, i.e. time series x_tFluctuation will be prior to time sequence Arrange y_tFluctuation, to Check-Out Time sequences y_tFluctuation whether prior to time series x_t, only need to be by x in above-mentioned algorithm_t, y_tPosition Exchange is put, repeats above-mentioned algorithm.

The source lotus complementary Benefit Evaluation Method spanning space-time of described consideration system bilateral randomness, it is characterized in that, the step In rapid (V) 2), build different prediction scenes and mainly consider three aspect factor；

(o) economic society factor, including steady politics, GDP increase, population increases；

(p) energy policy factor, including electric energy substitute policy, and the electrification of the countryside, Transportation electrification, electric heating equipment utilize Subsidy promotion efficiency, efficiency lifting policy；

(q) energy technology development factors, including electric energy substitute technology, DSM, efficiency lift technique.

The source lotus complementary Benefit Evaluation Method spanning space-time of described consideration system bilateral randomness, it is characterized in that, the step In rapid (V) 2), integrated forecasting refers to total using matched a variety of Individual forecast models for different prediction scenes With, including unit consumption of product method, output value unit consumption method, time series method, it is specially：

1. unit consumption of product method prediction process is：

The major product yield and the historical data of unit product power consumption that (I) is announced according to State Statistics Bureau or employer's organization Calculate the historical data of major product power consumption；

The major product yield and the history number of unit product power consumption that (II) is announced according to State Statistics Bureau or employer's organization It is predicted that major product yield, unit product power consumption and the major product power consumption in following each year；

The major product power consumption number that (III) announces the major product power consumption data that step (I) is calculated with middle Electricity Federation According to being contrasted, the deviation of the two is calculated；

The deviation in deflection forecast following each year that (IV) is calculated according to step (III)；

The deviation data that the major product power consumption that (V) is predicted according to step (II) is predicted with step (IV), prediction Following each year major product power consumption data consistent with middle Electricity Federation Statistical Criteria；

(VI) major product power consumption that Electricity Federation is announced in, trade power consumption amount data measuring and calculating major product power consumption account for The historical data of the proportion of trade power consumption amount；

The following each year major product power consumption of proportion historical data prediction that (VII) is calculated according to step (VI) accounts for industry The proportion of power consumption；

The proportion that the major product power consumption that (VIII) is predicted according to step (V) is predicted with step (VII), predict future Each year trade power consumption amount；

(IV) predicts that following each year industry value added, industry are used according to industry value added, the historical data of trade power consumption amount Electricity, and the result that the result is predicted with step (VIII) is mutually checked；

The industry value added that the trade power consumption amount that (X) is predicted according to step (VIII) is predicted with step (IV), prediction is not Carry out each year industry unit value added power consumption；uvwxyz

2. unit consumption of product method prediction process is：

(r) historical data of unit value added power consumption is calculated according to industry value added, the historical data of trade power consumption amount；

(s) following each year industry value added, list are predicted according to industry value added and unit value added power consumption historical data Position value added power consumption；

(t) according to following each year industry value added, unit value added power consumption, the trade power consumption amount in prediction following each year；

3. time series method is used to predict resident living power utility amount, according to domestic load growth trend per capita, with reference to people The change of mouth, you can prediction domestic load.

The source lotus complementary Benefit Evaluation Method spanning space-time of described consideration system bilateral randomness, it is characterized in that, the step It is rapid 3) in, source lotus complementation analysis model spanning space-time is spanning space-time mutually by wide area mainly for renewable energy power generation and load both sides Its smooth fluctuation of effect is mended, system both sides randomness is reduced, so as to improve system safe and stable operation efficiency and Electricity Investment Benefit.Build the trans-regional source lotus space-time complementary characteristic evaluation system of the statistical nature based on electric power big data, including step (Ⅵ)-(Ⅸ)：

(VI) step 1) and the power generation characteristics and part throttle characteristics of different regions in step 2) are combed, considers to include not With regional natural conditions, the energy structure difference external influence factors of regional disparity, in classification, assessment, prediction, correlation point On the basis of analysis, cluster, coordinate feature from source net lotus and regional complementarity carries out space-time complementation performance evaluation；

(VII) power supply complementary characteristic is assessed, and is permeated by fluctuation to be contemplated, gas-to electricity hourage, regenerative resource The analysis and assessment of the dimensions such as rate, regenerative resource output fraction；

(VIII) load complementary characteristic is assessed, complementary special by load fluctuation to be considered, kurtosis, season complementary characteristic, year The analysis and assessment of property dimension；

(Ⅸ) source lotus complementary characteristic is assessed, by regenerative resource accounting to be considered, peak-load regulating ability, complementary scale dimension Analysis and assessment.

The source lotus complementary Benefit Evaluation Method spanning space-time of described consideration system bilateral randomness, it is characterized in that, in (VI), Natural conditions mainly include the influences of the factor to generated output of renewable energy source such as weather and topography and geomorphology；Energy structure difference master To include the influences of the factor to electricity needs and part throttle characteristics such as the industrial structure, power mode；Regional disparity mainly includes can be again Raw energy electricity generation grid-connecting condition, generation technology, and demand response, electric energy substitute multiplexe electric technology maturity to power supply, load two The characteristic of side influences；

The data for considering to count, calculate include substantial amounts of causality data, the space-time data of higher-dimension, the monitoring of wide area Control, quick time response and real-time control data, in addition to the state of power system, it is also necessary to obtain and analyze related neck The data in domain, and uncertainty during process part shortage of data, thus it is theoretical using electric power big data, by source lotus basic data High in the clouds is aggregated into by big data acquisition technique, calculated by big data visualization technique, big data analysis and battle line technical Analysis The complementary index of each scheme, it is combined with multipotency stream complementation control technology, realizes the real-time optimization of energy resources with rationally matching somebody with somebody Put；

Every complementary index in step (VII)-(VIII), mainly describe to associate journey between two or more things A kind of index of degree, usual two or more things are complemented each other by certain contact, to improve overall efficacy, then claim it to have mutually Benefit property, the complementarity, is referred mainly between wind, light renewable energy power generation power, and different regions electric load in time domain Complementarity spatially；

Traditional space-time is complementary, refer mainly in certain period, be distributed in time two kinds of areal or The utilizability of two or more regenerative resources is different；Complementarity spatially is referred mainly in synchronization, different location Complementary possessed by one or more energy resources, complementation spanning space-time is on the basis of traditional space-time complementation, in region sky Between and time scale on extended, break through national boundaries and time zone limitation meet under global energy Background of Internet the whole world generate electricity money Source and the space-time complementary characteristic of load；

Regenerative resource has good space-time complementary in itself in wide scope, if having made full use of this complementation Property, then can effectively alleviate the intermittent of wind-powered electricity generation negatively affects with unstability to caused by power network, can only for solar energy Utilized on daytime, but by being networked across state, the time difference of 8 hours between China and Europe can be utilized to extend solar energy resources Usage time, while the load fluctuation in two areas can be stabilized, so as to reduce the randomness of source lotus both sides, improve system operation effect Benefit,

Specific complementary evaluation method is weighed with coefficient correlation, and coefficient correlation is for measuring between two stochastic variables The amount of correlation degree, more weak then its value of correlation degree are smaller, it is meant that complementary stronger, its calculation formula is：

In formula：R_XYFor coefficient correlation；X_t、Y_tRespectively two time serieses under the i of time scale interval；X₀、Y₀Respectively For sequence X_t、Y_tAverage value,

This time sequence can be understood in more detail by carrying out analysis to the poor Δ P (t) of the adjacent time inter of certain time series Fluctuation situation, calculation formula is：

Δ P (t)=P (t)-P (t-1) (20)

P (t) is the power output or load under time scale t in formula,

Smoothing effect coefficient S：Based on the standard deviation of generated output sequence, normalized with installed capacity and define smoothing effect system Number S,

Subscript " single " and " cluster " corresponding single game and the situation of cluster, the index are used to quantify cluster wind in formula Smoothness of the field relative to single game to scene fluctuation；

It is complementary in a steady stream in the step (VII)：Mainly for the solar energy of different regions, wind energy, water energy regenerative resource, According to the natural characteristic and changing rule of related energy resources, different time scales, the development of resources of hour, season, year are simulated Characteristic, typical energy source forms of electricity generation is chosen, different time scales are covered in foundation, and hour, season, the renewable energy power generation in year are special Property；The external factor such as different regions weather conditions, topography and geomorphology, grid-connected conditions, technology maturity are considered, based on electric power big data Statistical nature simulate following different type energy power curve；Emphasis is from output fluctuation, gas-to electricity hourage, renewable Its complementary characteristic of energy permeability, fraction etc.；

In the step (VIII), lotus lotus is complementary：Population, GDP levels mainly for different regions, the industrial structure, energization Ratio, per capita household electricity consumption factor, this area's future power consumption and peak load are predicted；In addition, used according to local history Electric load curve, with reference to the change of its industrial structure and power mode, electricity of the statistical nature based on electric power big data to future Power load curve is simulated, and grasps its different time scales, year, season, the moon, the maximum of day, minimum load and peak-valley difference information； Last emphasis carries out comprehensive analysis in terms of load fluctuation, kurtosis, season complementary and annual complementation；

In the step (Ⅸ), source lotus is complementary：On the basis of complementation, lotus lotus complementation analysis in a steady stream, considering as a whole can be again The raw energy is grid-connected with the mechanism that influences each other of local source net lotus, and the source lotus that analysis is contrasted between the different zones energy coordinates mark sheet It is existing, is being improved by regenerative resource permeability, is reducing peak-valley difference side for interregional source lotus complementation with electric power big data correlation theory The performance in face carries out integrated complementary evaluation.

It is excellent possessed by a kind of source lotus complementary Benefit Evaluation Method spanning space-time of consideration system bilateral randomness of the present invention Putting is：

1. the present invention under current power system source lotus both sides randomness, the background of the scale increase of fluctuation for maintaining The hot issue of the real-time dynamic power balance of system, construct comprising " lotus lotus is complementary ", " complementary in a steady stream " and " source lotus is complementary " The source lotus complementary evaluation system of three dimensions, it is comprehensive give to renewable energy power generation and load both sides by wide area across when Its smooth fluctuation of empty complementary effect, reduces system both sides randomness, so as to improve system safe and stable operation efficiency and electric power The evaluation scheme of returns of investment；

2. the present invention breaches country origin and territory restriction, take into full account regenerative resource with load wide out of global range Complementation in the range of domain, spanning space-time is distributed rationally, and source lotus complementary characteristic transnational, trans-regional, even across continent is carried out deeply Research, theory support is provided for the continuous propulsion of global energy Internet Strategy；

3. the present invention uses the theory and method of electric power big data, for the continuous expansion of Interconnection Scale, electric power, energy The multi-source of source and broad sense environment, polymorphic and isomeric data quantity exponentially increases and data disunity, missing problem, utilizes Electric power big data is theoretical, and source lotus basic data is aggregated into high in the clouds by big data acquisition technique, by big data visualization technique, Big data is analyzed and battle line technical Analysis calculates the complementary index of each scheme, is combined with multipotency stream complementation control technology, real The real-time optimization and reasonable disposition of existing energy resources；

4. its methodological science is reasonable, strong applicability, effect is good.

Brief description of the drawings

Fig. 1 is Wind turbines characteristics of output power curve synoptic diagram；

Fig. 2 is MCMC method modeling procedure schematic diagrames；

Fig. 3 is the source lotus complementary Benefit Evaluation Method flow chart spanning space-time of the consideration system bilateral randomness of the present invention.

Embodiment

The present invention is described in detail with reference to the accompanying drawings and examples.

Reference picture 1- Fig. 3, the source lotus complementary benefit evaluation side spanning space-time of consideration system bilateral randomness provided by the invention Method, comprise the following steps：

1) typical country or area or continent are chosen, for solar energy, wind energy, water energy clean energy resource, compiles difference The resource characteristicses that regional wind-powered electricity generation, solar power generation, water power clean energy resource generate electricity, are screened and clean energy resource distribution characteristics is portrayed in screening Attribute set；

2) according to the natural characteristic and changing rule of related energy resources, with reference to all kinds of generating set models, simulation hour/ The different cleaning power supply generated output characteristic of season/year Multiple Time Scales；

3) according to the energy resources feature and historical data of different zones, including countries/regions/continent, fitting is following should The typical power curve of clean energy resource generating and characteristic of country；

4) typical country or area or continent are chosen, is compiled including local population, economic development, the industrial structure, logical The basic datas such as electric ratio, per capita household electricity consumption, and load prediction key factor is screened based on data mining technology；

5) different prediction scenes are built from dimensions such as economic society factor, energy policy, energy technology development, foundation includes The integrated forecasting storehouse of a variety of Individual forecast models, load spy is carried out using matched model for different prediction scenes Property prediction, obtain following varying level year include power consumption, peak load, peak-valley difference part throttle characteristics；

6) power generation characteristics of above-mentioned different regions and part throttle characteristics are combed, consideration different regions natural conditions, Area's difference, the more external influence factors of energy structure difference, on the basis of classification, assessment, prediction, correlation analysis, cluster, from Source net lotus coordinates feature and regional complementarity carries out space-time complementation performance evaluation.

Above-mentioned steps 1) in, the attribute set statistics of clean energy resource distribution characteristics and analysis, comprise the following steps：

1.1) in the world, according to the clean energy resource resource distribution general status such as scene, typical be adapted to greatly is selected The country that scale development utilizes；

1.2) stock numbers such as wind speed, the intensity of illumination of the country are counted, specifying resource potential and technology can develop Amount.

Above-mentioned steps 2) in, resource characteristicses are converted into power producing characteristics, including following step with reference to all kinds of generating set models Suddenly：

2.1) Wind turbines power output is simulated, and detailed process is formula (1)-formula (2)；

2.2) wind power plant/group's power output simulation；

2.3) photovoltaic array power output is simulated, and detailed process participates in formula (3)-formula (5)

Above-mentioned steps 3) in, clean energy resource generating typical case's output that the following country is fitted according to historical data is bent Line and characteristic, comprise the following steps：

3.1) stochastic variable state is defined

The span of wind speed is wider in actual wind power plant, from 0m/s to 30m/s, under extreme case even more than 40m/s.When establishing wind speed time series models with MCMC methodology, suitable status number should be chosen according to wind speed profile scope first N, incorporated into according to the size of each time point wind speed to different states, original air speed data is converted into discrete state point. The state of stochastic variable defines the general method for taking decile span, and k-means methods can also be taken cluster Arrive；

3.2) structural regime transfer matrix

Wind farm wind velocity can be divided into different states, and what gustiness transfer characteristic described is exactly wind speed in different time Under yardstick, the probability nature that is shifted between different conditions.In order to quantify this characteristic, the concept of state-transition matrix is introduced. State-transition matrix P is N × N matrix, wherein, N is the status number divided to wind speed value scope.Each member in P Plain p_ijValue represents is wind speed current time as state i, be transferred to state j probability in subsequent time, i.e.,

p_ij=P (X_t=j | X_t-1=i) (6)

Corresponding to one step state transition matrix, defining multistep state-transition matrix is：

The matrix of other each state probability values is transferred to after n unit interval comprising current time state.

State-transition matrix defined above is single order battle array, that is, thinks air speed value and the current time of subsequent time Wind speed is relevant, and not related with a series of wind speed before.

After the wind speed time series of certain length is obtained, following steps generation wind farm wind velocity state can be taken to turn Move probability matrix：

A. the wind speed value scope of wind power plant is divided into N parts, per an a corresponding state.Wherein, N is status number；

B. N × N null matrix S is established, to count the number shifted between each state；

C. first value corresponding states m, next moment value corresponding states n of wind farm wind velocity actual measurement sequence are assumed, then Matrix S corresponding element adds 1；

D. step c is applied to remaining all adjacent air speed value in sequence, all corresponding shapes is contained in obtained S State transfer number；

E. to obtain state-transition matrix P, by each element in S divided by the element sum being expert at, i.e.,

Thus, the step state transition probability matrix P of wind farm wind velocity one has been obtained.If intentionally get multistep state transfer square Battle array, as long as the subsequent time value in step c is changed into lower k moment value.

3.3) cumulative probability transfer matrix is constructed

Above on state-transition matrix P basis, the building method of cumulative probability transfer matrix is formula (9)：

In formula, if P dimension is N × N, dimension be N × (N+1), the first row of accumulated probability transfer matrix is equal Be zero, since secondary series, the values of each element m rows n row be element of the m row, column number less than n corresponding in matrix P it With.

3.4) stochastic regime sequence is generated

The step of generating stochastic regime sequence with DSMC using accumulated probability transfer matrix is as follows：

A. set current wind speed and be in state m；

B. the uniform random number u on (0,1) section is generated；

C. by u with m row elements (element line number is current wind speed status number) compared with, if meeting relation Formula, then next state be taken as n；

D. if the stochastic regime sequence of generation has met length requirement, then stop；Conversely, current state is changed into n, return Step b is returned to continue.

3.5) wind farm wind velocity time series is constructed

The time series generated according to the method described above is the status number of series of discrete, it is necessary to take certain method will It is converted to wind farm wind velocity time series,

X=X_n,min+u(X_n,max-X_n,min) (10)

In formula, state n is corresponding wind speed, and X is the simulation wind speed at the moment, X_n,max、X_n,minIt is that state n is covered respectively The upper and lower bound of lid wind speed interval.

Above-mentioned steps 4) in, load prediction key factor is screened based on data mining technology, comprised the following steps：

4.1) stationary test

Generally, it is the base that whether there is stable relation between analysis time sequence that stationary test is carried out to each time series Plinth.Stationary time series refers to the time series with stable statistical law, i.e., the desired value of time series and variance not with Time change, sequence data fluctuate near desired value.It is non-when reflecting explanation time series and by explanation time series relation Stationary time series can cause False value, i.e. " shadowing property ".It is preferable that shadowing property represents that explanatory variable and explained variable have Mathematical Fitting characteristic, but balanced relation steady in a long-term is not present between them.When with shadowing property explanatory variable to being solved When releasing variable and being modeled prediction, generally there is larger deviation in predicted value and actual value.

In actual analysis research, the method for time series stationary test has a variety of：Based on sample sequence auto-correlation letter Number method；Scatterplot is stranded method；Unit root test method.Unit root test method is the steady domestic animal method of inspection of time series the most frequently used at present, Judge the stationarity of time series by judging time series to be tested with the presence or absence of unit root.ADF methods of inspection (Augmented Dickey-Fuller Test) is current most common unit root test method, and algorithm is as follows：

Check-Out Time sequence is treated by following regression equation to be fitted:

T=(ρ ' -1)/se (ρ ') (14)

In formula, β, η t represent intercept and trend term in time series to be tested, the choosing of regression equation (11)-(13) respectively Selecting can select according to the presence or absence of intercept item in time series to be tested and trend term；L represents lag order, l in regression equation Value generally determines by AIC minimum criterias, ε_tFor residual error item.Hysteresis item is added in regression equationIt is able to ensure that ε_t For white noise.In T normalized set formulas, ρ ' represents ρ least-squares estimation, and se (ρ ') represents that ρ ' unbiaseds help meter.Assuming that H₀Represent that former time series is nonstationary time series, alternative hypothesis H₁Expression time series is stationary time series.According to calculating T statistics value, if assume H₀Set up, then former time series is nonstationary time series, conversely, refusal null hypothesis, i.e. time Sequence is stationary time series.

Found after carrying out stationary test to the index in index storehouse, electric power market demand index is the single order non-stationary time Sequence.In econometrics model, judge for the relation between nonstationary time series, can be referred to by co integration test to verify It whether there is relation steady in a long-term between mark, the premise of inspection is that each time series must be the unstable time series of same order. Therefore, after carrying out stationary test to the index time series in achievement data storehouse, reject non-flat not for same order with electricity needs Steady time series.Co-integration test will be carried out with the time series of electricity needs same order non-stationary, passes through co-integration test Time series then proves relation steady in a long-term be present with electricity needs.

4.2) co integration test

Whole opinion of burying is assisted to think there may be relation steady in a long-term between the time series of several non-stationaries, on condition that this Several nonstationary time series belong to the unstable time series of same order, and condition is that and if only if these same order non-stationary times The linear combination of sequence is stationary time series.Such as time series x_tWith y_t, belong to the time serieses of n rank non-stationaries, and this The linear combination α of two time serieses₁x_t+α₂y_tIt is that n-m ranks are singly whole, then it is assumed that time series x_tWith y_tIn the presence of steady in a long-term Relation.

By the stationary test of aforesaid time sequence, filter out when belonging to the whole non-stationary of same order list with electricity needs Between sequence, belong to whether the whole nonstationary time series of same order list with electricity needs has relation steady in a long-term with electricity needs It can be tested by Co-integration Theory.

4.3) Granger causality test

The causality that sequencing is referred to as between time series, Granger causality test are fluctuated between time series It is the common method for examining this relation.Time series x_t, y_tBetween fluctuate sequencing performance be time series x_t, to time sequence Arrange y_tPrediction it is helpful, and there are stronger dependency relations in two time serieses.The principle of Granger CaFpngerusality test is as follows： By making hypothesis testing, Check-Out Time sequence x to regression analysis_tPast value to time series y_tPresent value can do much journeys The explanation of degree.Co-integration relationship calculating process is as follows：

A. to time variable y_tVAR (l) models are established, calculate residual sum of squares (RSS) RSS_R

B. x is added in VAR models_tLag item x_t-jIt is shown below, and calculates RSS_UR

C. make to assume：H₀:b₁=b₂=...=b_q=0, i.e. x_tHysteresis item be not belonging to y_tRecurrence in

D. F test statistics is calculated

E. by the critical value F in F values and the α levels of signifiance_αCompare, if F ＞ F_α, refusal hypothesis H₀

In F examines formula, n represents the dimension of sample to be tested, and q represents time series x_tHysteresis item number, k represent treat Estimate the number of parameter, three obeys the F distributions that the free degree is q and (n-k).Refuse H₀Represent, hysteresis item x_tBelong to former recurrence side Journey, add hysteresis item x_tTime series x can be improved_tPrediction effect, i.e. time series x_tFluctuation will be prior to time series y_t's Fluctuation.To Check-Out Time sequences y_tFluctuation whether prior to time series x_t, only need to be by x in above-mentioned algorithm_t, y_tPosition it is mutual Change, repeat above-mentioned algorithm.

Above-mentioned steps 5) in, part throttle characteristics prediction is carried out using matched model for different prediction scenes, Comprise the following steps：

5.1) different prediction scenes are built, it is main to consider economic society factor, including steady politics, GDP increase, population Increase；Energy policy factor, including electric energy substitute policy (being utilized etc. such as the electrification of the countryside, Transportation electrification, electric heating equipment Subsidize promotion efficiency), efficiency lifting policy；C. energy technology development factors, including electric energy substitute technology, dsm skill Art, efficiency lift technique；

5.2) integrated forecasting refers to uses matched a variety of Individual forecast model summations for different prediction scenes, Such as unit consumption of product method, output value unit consumption method, time series method.

Above-mentioned steps 6) in, for source net lotus coordination feature and regional complementarity progress space-time complementation performance evaluation, including with Lower step：

6.1) it is theoretical using electric power big data, source lotus basic data is aggregated into high in the clouds by big data acquisition technique, by Big data visualization technique, big data analysis and battle line technical Analysis calculate the complementary index of each scheme, complementary with multipotency stream Control technology is combined, and realizes the real-time optimization and reasonable disposition of energy resources.

6.2) complementary evaluation in a steady stream is carried out：Mainly for the solar energy of different regions, wind energy, water can etc. regenerative resource, According to the natural characteristic and changing rule of related energy resources, the development of resources of simulation different time scales (hour, season, year) Characteristic, typical energy source forms of electricity generation is chosen, establish the renewable energy power generation for covering different time scales (hour, season, year) Characteristic；Different regions weather conditions, topography and geomorphology, grid-connected conditions, technology maturity external factor are considered, based on electric power big data Statistical nature simulate following different type energy power curve；Emphasis is from output fluctuation, gas-to electricity hourage, renewable Its complementary characteristic in terms of energy permeability, fraction.

Output fluctuation index：Carrying out analysis to the poor Δ P (t) of the adjacent time inter of certain time series can be in more detail Understand the fluctuation situation of this time sequence, be calculated as follows：

Δ P (t)=P (t)-P (t-1) (20)

P (t) is the power output under time scale t in formula.

Smoothing effect coefficient S：Standard deviation (being normalized with installed capacity) based on generated output sequence defines smoothing effect Coefficient S.

Gas-to electricity hourage index：Be in the regular period (1 year) average generating equipment capacity in oepration at full load condition Under hours of operation.Average generating equipment utilizes the average generating equipment capacity of hour=report period generated energy/report period.

Regenerative resource permeability index：One regional regenerative resource installation/local peak load.

Fraction index：It can be the dependable capacity that system provides to weigh the generation of electricity by new energy when carrying out power balance analysis. The new energy of load peak period is contributed by sorting from big to small, the minimum of (such as 95%) wind-powered electricity generation goes out under a certain fraction Power.On wind power output curve i.e. after sequence, time shaft is that new energy is contributed corresponding at 0.95.

6.3) lotus lotus complementary evaluation is carried out：Population, GDP levels, the industrial structure, energization ratio mainly for different regions The factors such as example, per capita household electricity consumption, this area's future power consumption and peak load are predicted；In addition, used according to local history Electric load curve, with reference to the change of its industrial structure and power mode, electricity of the statistical nature based on electric power big data to future Power load curve is simulated, and grasps the letter such as maximum, minimum load and peak-valley difference of its different time scales (year, season, the moon, day) Breath；Last emphasis carries out comprehensive analysis in terms of load fluctuation, kurtosis, season complementary and annual complementation.

Load fluctuation index：Carrying out analysis to the poor Δ L (t) of the adjacent time inter of certain time series can be in more detail Understand the fluctuation situation of this time sequence.

Kurtosis index：Peak load, minimum load in certain period of time, with peak-valley difference.

Season is complementary：In one season, the complementary characteristic of different regions load curve, including peak load, minimum load And change of peak-valley difference etc..

It is annual complementary：In 1 year, the complementary characteristic of different regions load curve, including peak load, minimum load and peak Change of paddy difference etc..

6.4) source lotus complementary evaluation is carried out：On the basis of complementation, lotus lotus complementation analysis in a steady stream, renewable energy is considered as a whole Source is grid-connected with the mechanism that influences each other of local source net lotus, and the source lotus that analysis is contrasted between the different zones energy coordinates feature performance, fortune With electric power big data correlation theory, source lotus complementation is in terms of regenerative resource permeability is improved, reduce peak-valley difference between survey region Performance, carry out integrated complementary evaluation.

The present invention provides a kind of source lotus complementary benefit spanning space-time of consideration system bilateral randomness suitable for methods described Evaluation system, including：Different cleaning power supply typical case output characteristic statistical analysis module spanning space-time, for compiling not The resource characteristicses to be generated electricity with clean energy resourcies such as regional wind-powered electricity generation, solar power generations, are screened and clean energy resource distribution characteristics is portrayed in screening Attribute set, and combine all kinds of generating set models and MCMC models, simulate following different time scales different cleaning power supply Generated output characteristic；Load prediction module based on Data Mining technology, for country variant regional load prediction it is crucial because Element is screened, and different prediction scenes is built from economic society factor, energy policy, energy technology development dimension, for difference Prediction scene part throttle characteristics prediction is carried out using matched model, obtain including in following varying level year power consumption, Peak load, peak-valley difference part throttle characteristics；The trans-regional source lotus space-time complementary characteristic of statistical nature based on electric power big data is assessed Module, for considering different regions natural conditions, regional disparity, the more external influence factors of energy structure difference, classifying, commenting Estimate, predict, on the basis of correlation analysis, cluster, wide area complementation spanning space-time is passed through to renewable energy power generation and load both sides Its smooth fluctuation of effect, reduces system both sides randomness, so as to improve system safe and stable operation efficiency and Electricity Investment effect Benefit.

Above-mentioned embodiment is merely to illustrate the present invention, not exhaustive, every on the basis of technical solution of the present invention The equivalents of upper progress and improvement, belong to the scope of the claims in the present invention protection.

Claims (11)

1. a kind of source lotus complementary Benefit Evaluation Method spanning space-time of consideration system bilateral randomness, it is characterized in that, including the step of Have：

1) different cleaning power supply typical case output property data base spanning space-time is built；

2) load forecasting model based on Data Mining technology is established, and considers that polynary influence factor predicts that following different regions are born Lotus characteristic；

3) more external influence factors such as different regions natural conditions, regional disparity, energy structure difference are considered, structure is based on electric power The trans-regional source lotus space-time complementary characteristic evaluation system of the statistical nature of big data.

2. the source lotus complementary Benefit Evaluation Method spanning space-time of consideration system bilateral randomness according to claim 1, it is special Sign is that structure different cleaning power supply typical case output property data base spanning space-time, is comprised the step of in the step 1)：

(Ι) chooses typical country or area, for solar energy, wind energy, water energy clean energy resource, compile different regions wind-powered electricity generation, The resource characteristicses that solar power generation, water power clean energy resource generate electricity, screen and screen the property set for portraying clean energy resource distribution characteristics Close；

(II) according to the natural characteristic and changing rule of related energy resources, with reference to all kinds of generating set models, hour/season is simulated The different cleaning power supply generated output characteristic of section/year Multiple Time Scales；

(III) according to the energy resources feature and historical data of different zones, i.e. countries/regions/continent, it is fitted the following country Or the typical power curve of the clean energy resource in area or continent generating and characteristic.

3. the source lotus complementary Benefit Evaluation Method spanning space-time of consideration system bilateral randomness according to claim 1 or 2, its It is characterized in, it is necessary in the world, according to honourable clean energy resource resource distribution general status, choose in (Ι) of the step 1) The typical country or continent for being adapted to large-scale develop and utilize of choosing, count the wind speed, light of the country or continent According to intensity stock number, resource potential and technical exploitation amount are specified.

4. the source lotus complementary Benefit Evaluation Method spanning space-time of consideration system bilateral randomness according to claim 1 or 2, its It is characterized in, in (II) of the step 1), passes through Wind turbines output power model, wind power plant/group's output power model, photovoltaic Wind energy and solar energy resources characteristic are converted into wind-powered electricity generation and solar power generation power producing characteristics by array output power model respectively, described The calculating formula of model is formula (1)-formula (5)：

1. Wind turbines output power model

For Wind turbines, containing asynchronous generator, doubly fed induction generator and synchronous induction motor, Wind turbines contribute with wind speed it Between functional relation, wherein, P_rFor the rated power of wind power generating set, V_ciTo cut wind speed, V_rFor rated wind speed, V_coTo cut Go out wind speed,

Wind turbines power output P_WFunctional relation between wind speed v is formula (1)：

<mrow> <msub> <mi>P</mi> <mi>W</mi> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mi>v</mi> <mo>&amp;le;</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>A</mi> <mo>+</mo> <mi>B</mi> <mi>v</mi> <mo>+</mo> <msup> <mi>Cv</mi> <mn>2</mn> </msup> </mrow> </mtd> <mtd> <mrow> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mo>&amp;le;</mo> <mi>v</mi> <mo>&amp;le;</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <msub> <mi>P</mi> <mi>r</mi> </msub> </mtd> <mtd> <mrow> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>&lt;</mo> <mi>v</mi> <mo>&amp;le;</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>o</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mi>v</mi> <mo>&gt;</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>o</mi> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>

A, B, C are Wind turbines power characteristic parameter, and different Wind turbines can be slightly different, and expression formula is formula (2)：

<mrow> <mtable> <mtr> <mtd> <mrow> <mi>A</mi> <mo>=</mo> <mfrac> <mn>1</mn> <msup> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mfrac> <mo>&amp;lsqb;</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mn>4</mn> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> <msup> <mrow> <mo>&amp;lsqb;</mo> <mfrac> <mrow> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> <mrow> <mn>2</mn> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> </mfrac> <mo>&amp;rsqb;</mo> </mrow> <mn>3</mn> </msup> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>B</mi> <mo>=</mo> <mfrac> <mn>1</mn> <msup> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mfrac> <mo>&amp;lsqb;</mo> <mn>4</mn> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> <msup> <mrow> <mo>&amp;lsqb;</mo> <mfrac> <mrow> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> <mrow> <mn>2</mn> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> </mfrac> <mo>&amp;rsqb;</mo> </mrow> <mn>3</mn> </msup> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>C</mi> <mo>=</mo> <mfrac> <mn>1</mn> <msup> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mfrac> <mo>&amp;lsqb;</mo> <mn>2</mn> <mo>-</mo> <mn>4</mn> <msup> <mrow> <mo>&amp;lsqb;</mo> <mfrac> <mrow> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> <mrow> <mn>2</mn> <msub> <mi>v</mi> <mi>r</mi> </msub> </mrow> </mfrac> <mo>&amp;rsqb;</mo> </mrow> <mn>3</mn> </msup> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>

2. wind power plant/group's output power model

Wind power plant is formed by being installed in parallel in tens of same place even typhoon group of motors up to a hundred, due to the influence of wake flow, The wind speed for being located Wind turbines on the leeward will be less than being seated in the wind speed of wind upwind group of motors, and referred to as wake flow is imitated Should, it is determined that must take into consideration wake effect during Power Output for Wind Power Field, common wake effect model have Jensen models and Lissaman models, the Wind turbines in level terrain can use Jensen model analysis, the Wind turbines in complicated landform Lissaman modelings can be used, for the wind power plant of planning stage, it is assumed that the wind speed of all Wind turbines in same wind power plant Identical with wind direction, it is the wind power plant to be multiplied by a coefficient for representing wake effect with all Wind turbines gross output sums Power output,

The operating experience of wind power plant shows, can be lost by rational deployment to reduce wake effect, the typical case that wake flow causes damage Value is 10%, and the gross output of Wind turbines now is multiplied by into canonical system numerical value 0.9 to represent the real output of wind power plant；

3. photovoltaic array output power model

The power output of photovoltaic array depends on intensity of solar radiation, array area and electricity conversion, has M for one The photovoltaic array of individual battery component, the area and photoelectric transformation efficiency of each component are respectively A_mAnd η_m(m=1,2 ..., M), then The power output of photovoltaic array is formula (3)-formula (5)：

P_solar=IA η (3)

<mrow> <mi>A</mi> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msub> <mi>A</mi> <mi>m</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>

<mrow> <mi>&amp;eta;</mi> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msub> <mi>A</mi> <mi>m</mi> </msub> <msub> <mi>&amp;eta;</mi> <mi>m</mi> </msub> </mrow> <mi>A</mi> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>

In formula, A and η are respectively the gross area of array and equivalent photoelectric transformation efficiency.

5. the source lotus complementary Benefit Evaluation Method spanning space-time of consideration system bilateral randomness according to claim 1 or 2, its It is characterized in, in the step 1) (III), passes through country variant, area or the new energy in continent historical statistical data and characteristic, base In following different regions new energy power producing characteristics of MCMC model inversions simulation, the specific construction method of model is：

1. define stochastic variable state

The span of wind speed is wider in actual wind power plant, and from 0m/s to 30m/s, 40m/s is even more than under extreme case, When establishing wind speed time series models with MCMC methodology, suitable status number N should be chosen according to wind speed profile scope first, according to The size of each time point wind speed is incorporated into different states, and original air speed data is converted into discrete state point, random to become The state of amount defines the method for taking decile span, also k-means methods can be taken to be clustered to obtain；

2. structural regime transfer matrix

Wind farm wind velocity can be divided into different states, and what gustiness transfer characteristic described is exactly wind speed in different time scales Under, the probability nature that is shifted between different conditions in order to quantify this characteristic, introduces the concept of state-transition matrix, state Transfer matrix P is N × N matrix, wherein, N is the status number divided to wind speed value scope, each element p in P_ij Value represents is wind speed current time as state i, be transferred to state j probability in subsequent time, i.e.,

p_ij=P (X_t=j | X_t-1=i) (6)

Corresponding to one step state transition matrix, defining multistep state-transition matrix is：

<mrow> <msubsup> <mi>p</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mi>n</mi> </msubsup> <mo>=</mo> <mi>P</mi> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>t</mi> </msub> <mo>=</mo> <mi>j</mi> <mo>|</mo> <msub> <mi>X</mi> <mrow> <mi>t</mi> <mo>-</mo> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow>

The matrix of other each state probability values is transferred to after n unit interval comprising current time state,

The state-transition matrix that formula (6) and formula (7) define is single order battle array, that is, think subsequent time air speed value only with it is current when The wind speed at quarter is relevant, and not related with a series of wind speed before,

After the wind speed time series of certain length is obtained, the generation wind farm wind velocity state transfer of (a)-(e) steps is taken Probability matrix：

(a) the wind speed value scope of wind power plant is divided into N parts, per an a corresponding state, wherein, N is status number；

(b) N × N null matrix S is established, to count the number shifted between each state；

(c) first value corresponding states m, next moment value corresponding states n of wind farm wind velocity actual measurement sequence are assumed, then matrix S corresponding element adds 1；

(d) step (c) is applied to remaining all adjacent air speed value in sequence, all corresponding shapes is contained in obtained S State transfer number；

(e) to obtain state-transition matrix P, by each element in S divided by the element sum being expert at, i.e.,

<mrow> <msub> <mi>p</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <mfrac> <msub> <mi>s</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>s</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow>

Thus, the step state transition probability matrix P of wind farm wind velocity one has been obtained.If intentionally getting multistep state-transition matrix, only Subsequent time value in step (c) is changed to lower k moment value；

3. construct cumulative probability transfer matrix

Above on state-transition matrix P basis, the building method of cumulative probability transfer matrix is formula (9)：

<mrow> <msub> <mi>P</mi> <mrow> <mi>c</mi> <mi>u</mi> <mi>m</mi> <mo>,</mo> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>&lt;</mo> <mi>n</mi> </mrow> </munder> <msub> <mi>p</mi> <mrow> <mi>m</mi> <mi>j</mi> </mrow> </msub> </mrow> </mtd> <mtd> <mrow> <mn>1</mn> <mo>&lt;</mo> <mi>n</mi> <mo>&amp;le;</mo> <mi>N</mi> <mo>+</mo> <mn>1</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>

In formula, if P dimension is N × N, dimension be N × (N+1), the first row of accumulated probability transfer matrix is zero, Since secondary series, the value of each element m rows n row is the element sum that m row, column number corresponding in matrix P is less than n；

4. generate stochastic regime sequence

It is (f)-(i) using the step of accumulated probability transfer matrix and DSMC generation stochastic regime sequence：

(f) set current wind speed and be in state m；

(g) the uniform random number u on (0,1) section is generated；

(h) by u and m row elements, element line number is that current wind speed status number is compared, if meeting relational expression, Next state is taken as n；

If (i) the stochastic regime sequence of generation has met length requirement, stop；Conversely, current state is changed into n, return Step (g) continues；

5. construct wind farm wind velocity time series

By the time series of generation it is the status number of series of discrete, it is necessary to take certain method to be converted into wind power plant Wind speed time series,

X=X_n,min+u(X_n,max-X_n,min) (10)

In formula, state n is corresponding wind speed, and X is the simulation wind speed at the moment, X_n,max、X_n,minIt is that state n covers wind speed respectively The upper and lower bound in section.

6. the source lotus complementary Benefit Evaluation Method spanning space-time of consideration system bilateral randomness according to claim 1, it is special Sign is to consider that polynary influence factor predicts following different regions part throttle characteristics in the step 2), and foundation is based on Data Mining skill The load forecasting model of art, and consider that polynary influence factor predicts following different regions part throttle characteristics, comprise the following steps：

(IV) typical country or area are chosen, is compiled including local population, economic development, the industrial structure, energization ratio, people The basic datas such as equal power consumption, and load prediction key factor is screened based on data mining technology；

(V) different prediction scenes are built from the dimension of economic society factor, energy policy, energy technology development, established comprising more The integrated forecasting storehouse of kind Individual forecast model, part throttle characteristics is carried out using matched model for different prediction scenes Prediction, obtain including power consumption, peak load, the part throttle characteristics of peak-valley difference in following varying level year.

7. the source lotus complementary Benefit Evaluation Method spanning space-time of the consideration system bilateral randomness according to claim 1 or 6, its It is characterized in, in (IV) of the step 2), data mining is that the various analysis tools of utilization find model in mass data The process of relation between data, can from data automatically decimation pattern, association, change, abnormal and significant structure, its It is worth and is to improve forecast model using data mining technology,

Load prediction key factor, which is screened, based on data mining technology mainly includes the foundation of alternative index storehouse, achievement data Cleaning, stationarity verification, classification of being tested to alternative index, co-integration relationship text, Granger causality test process are：

1. stationary test

It is the basis that whether there is stable relation between analysis time sequence that stationary test is carried out to each time series, when steady Between sequence refer to the time series with stable statistical law, i.e. the desired value of time series and variance does not change over time, Sequence data fluctuates near desired value, when reflecting explanation time series and by explanation time series relation, the non-stationary time Sequence can cause False value, i.e. " shadowing property ", and shadowing property represents that there is preferable mathematics to intend for explanatory variable and explained variable Characteristic is closed, but balanced relation steady in a long-term is not present between them, explained variable is entered when with shadowing property explanatory variable During row modeling and forecasting, generally there is larger deviation in predicted value and actual value,

It is using ADF methods of inspection (Augmented Dickey-Fuller Test) unit root test method, i.e., to be checked by judging Time series is tested with the presence or absence of unit root to judge the stationarity of time series, algorithm is formula (11)-formula (15)：

Check-Out Time sequence is treated by regression equation to be fitted:

<mrow> <msub> <mi>&amp;Delta;y</mi> <mi>t</mi> </msub> <mo>=</mo> <msub> <mi>&amp;rho;y</mi> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>l</mi> </munderover> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <msub> <mi>&amp;Delta;y</mi> <mrow> <mi>t</mi> <mo>-</mo> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;epsiv;</mi> <mi>t</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>

<mrow> <msub> <mi>&amp;Delta;y</mi> <mi>t</mi> </msub> <mo>=</mo> <mi>&amp;beta;</mi> <mo>+</mo> <msub> <mi>&amp;rho;y</mi> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>l</mi> </munderover> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <msub> <mi>&amp;Delta;y</mi> <mrow> <mi>t</mi> <mo>-</mo> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;epsiv;</mi> <mi>t</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>12</mn> <mo>)</mo> </mrow> </mrow>

<mrow> <msub> <mi>&amp;Delta;y</mi> <mi>t</mi> </msub> <mo>=</mo> <mi>&amp;beta;</mi> <mo>+</mo> <mi>&amp;eta;</mi> <mi>t</mi> <mo>+</mo> <msub> <mi>&amp;rho;y</mi> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>l</mi> </munderover> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <msub> <mi>&amp;Delta;y</mi> <mrow> <mi>t</mi> <mo>-</mo> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;epsiv;</mi> <mi>t</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow>

T=(ρ ' -1)/se (ρ ') (14)

In formula, β, η t represent intercept and trend term in time series to be tested, the choosing of regression equation (11)-formula (13) respectively Selecting can select according to the presence or absence of intercept item in time series to be tested and trend term；L represents lag order, l in regression equation Value generally determines by AIC minimum criterias, ε_tFor residual error item, hysteresis item is added in regression equationIt is able to ensure that ε_t For white noise, in T normalized set formulas, ρ ' represents ρ least-squares estimation, and se (ρ ') represents that ρ ' unbiaseds help meter, it is assumed that H₀Represent that former time series is nonstationary time series, alternative hypothesis H₁Expression time series is stationary time series, according to calculating T statistics value, if assume H₀Set up, then former time series is nonstationary time series, conversely, refusal null hypothesis, i.e. time Sequence is stationary time series.

Found after carrying out stationary test to the index in index storehouse, electric power market demand index is single order non-stationary time sequence Arrange, in econometrics model, judge for the relation between nonstationary time series, index can be verified by co integration test Between whether there is relation steady in a long-term, the premise of inspection is that each time series must be the unstable time series of same order, because This, after carrying out stationary test to the index time series in achievement data storehouse, it is not same order non-stationary to reject with electricity needs Time series, will carry out co-integration test with the time series of electricity needs same order non-stationary, by co-integration test when Between sequence then prove relation steady in a long-term be present with electricity needs；

2. co integration test

Whole opinion of burying is assisted to think there may be relation steady in a long-term between the time series of several non-stationaries, on condition that these Nonstationary time series belongs to the unstable time series of same order, condition is that and if only if these same order nonstationary time series Linear combination be stationary time series, such as time series x_tWith y_t, belong to the time serieses of n rank non-stationaries, and the two The linear combination α of time series₁x_t+α₂y_tIt is that n-m ranks are singly whole, then it is assumed that time series x_tWith y_tIn the presence of pass steady in a long-term System；

The stationary test of passage time sequence, filter out and belonged to the whole nonstationary time series of same order list with electricity needs, Belonging to the whole nonstationary time series of same order list with electricity needs and whether relation steady in a long-term being present with electricity needs to pass through Co-integration Theory is tested；

3. Granger causality test

The causality that sequencing is referred to as between time series is fluctuated between time series, is examined using Granger causality Test, time series x_t, y_tBetween fluctuate sequencing performance be time series x_t, to time series y_tPrediction it is helpful, and two There is stronger dependency relation in individual time series, the principle of Granger CaFpngerusality test is：By making hypothesis testing to regression analysis, Check-Out Time sequence x_tPast value to time series y_tPresent value can do the explanation of much degree, and co-integration relationship calculated Cheng Wei：

(j) to time variable y_tVAR (l) models are established, calculate residual sum of squares (RSS) RSS_R

<mrow> <msub> <mi>y</mi> <mi>t</mi> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>l</mi> </munderover> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <msub> <mi>y</mi> <mrow> <mi>t</mi> <mo>-</mo> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;epsiv;</mi> <mi>t</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>16</mn> <mo>)</mo> </mrow> </mrow>

(k) x is added in VAR models_tLag item x_t-jIt is shown below, and calculates RSS_UR

<mrow> <msub> <mi>y</mi> <mi>t</mi> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>l</mi> </munderover> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <msub> <mi>y</mi> <mrow> <mi>t</mi> <mo>-</mo> <mi>i</mi> </mrow> </msub> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>q</mi> </munderover> <msub> <mi>b</mi> <mi>i</mi> </msub> <msub> <mi>x</mi> <mrow> <mi>t</mi> <mo>-</mo> <mi>j</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>&amp;epsiv;</mi> <mi>t</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>17</mn> <mo>)</mo> </mrow> </mrow>

(l) assume：H₀:b₁=b₂=...=b_q=0, i.e. x_tHysteresis item be not belonging to y_tRecurrence in

(m) F test statistics is calculated

<mrow> <mi>F</mi> <mo>=</mo> <mfrac> <mrow> <mo>(</mo> <msub> <mi>RSS</mi> <mi>R</mi> </msub> <mo>-</mo> <msub> <mi>RSS</mi> <mrow> <mi>U</mi> <mi>R</mi> </mrow> </msub> <mo>)</mo> <mo>/</mo> <mi>q</mi> </mrow> <mrow> <msub> <mi>RSS</mi> <mrow> <mi>U</mi> <mi>R</mi> </mrow> </msub> <mo>/</mo> <mrow> <mo>(</mo> <mi>n</mi> <mo>-</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>18</mn> <mo>)</mo> </mrow> </mrow>

(n) by the critical value F in F values and the α levels of signifiance_αCompare, if F ＞ F_α, refusal hypothesis H₀,

In F examines formula, n represents the dimension of sample to be tested, and q represents time series x_tHysteresis item number, k represent it is to be estimated The number of parameter, n, q and k three obey the F distributions that the free degree is q and (n-k), refuse H₀Represent, hysteresis item x_tBelong to former recurrence Equation, add hysteresis item x_tTime series x can be improved_tPrediction effect, i.e. time series x_tFluctuation will be prior to time series y_t Fluctuation, to Check-Out Time sequences y_tFluctuation whether prior to time series x_t, only need to be by x in above-mentioned algorithm_t, y_tPosition Exchange, repeat above-mentioned algorithm.

8. the source lotus complementary Benefit Evaluation Method spanning space-time of the consideration system bilateral randomness according to claim 1 or 6, its It is characterized in, in (V) of the step 2), builds different prediction scenes and mainly consider three aspect factor；

(o) economic society factor, including steady politics, GDP increase, population increases；

(p) energy policy factor, including electric energy substitute policy, the benefit that the electrification of the countryside, Transportation electrification, electric heating equipment utilize Paste promotion efficiency, efficiency lifting policy；

(q) energy technology development factors, including electric energy substitute technology, DSM, efficiency lift technique.

9. the source lotus complementary benefit evaluation side spanning space-time of the consideration system bilateral randomness according to claim 1 or 6 or 8 Method, it is characterized in that, in (V) of the step 2), integrated forecasting refers to for different prediction scenes using matched more Kind Individual forecast model summation, including unit consumption of product method, output value unit consumption method, time series method, it is specially：

1. unit consumption of product method prediction process is：

The major product yield and the historical data of unit product power consumption that (I) is announced according to State Statistics Bureau or employer's organization are calculated The historical data of major product power consumption；

The major product yield and the historical data of unit product power consumption that (II) is announced according to State Statistics Bureau or employer's organization are pre- Survey major product yield, unit product power consumption and the major product power consumption in following each year；

(III) enters the major product power consumption data that the major product power consumption data that step (I) is calculated are announced with middle Electricity Federation Row contrast, calculates the deviation of the two；

The deviation in deflection forecast following each year that (IV) is calculated according to step (III)；

The deviation data that the major product power consumption that (V) is predicted according to step (II) is predicted with step (IV), predict future Each year major product power consumption data consistent with middle Electricity Federation Statistical Criteria；

(VI) major product power consumption that Electricity Federation is announced in, trade power consumption amount data measuring and calculating major product power consumption account for industry The historical data of the proportion of power consumption；

The following each year major product power consumption of proportion historical data prediction that (VII) is calculated according to step (VI) accounts for trade power consumption The proportion of amount；

The proportion that the major product power consumption that (VIII) is predicted according to step (V) is predicted with step (VII), prediction following each year Trade power consumption amount；

(IV) predicts following each year industry value added, trade power consumption amount according to industry value added, the historical data of trade power consumption amount, And the result for being predicted the result with step (VIII) is mutually checked；

The industry value added that the trade power consumption amount that (X) is predicted according to step (VIII) is predicted with step (IV), predict following each Year industry unit value added power consumption；uvwxyz

2. unit consumption of product method prediction process is：

(r) historical data of unit value added power consumption is calculated according to industry value added, the historical data of trade power consumption amount；

(s) predict that following each year industry value added, unit increase according to industry value added and unit value added power consumption historical data Value added power consumption；

(t) according to following each year industry value added, unit value added power consumption, the trade power consumption amount in prediction following each year；

3. time series method is used to predict resident living power utility amount, according to domestic load growth trend per capita, with reference to population Change, you can prediction domestic load.

10. the source lotus complementary Benefit Evaluation Method spanning space-time of consideration system bilateral randomness according to claim 1, it is special Sign is, in the step 3), source lotus complementation analysis model spanning space-time passes through mainly for renewable energy power generation and load both sides Its smooth fluctuation of wide area complementary effect spanning space-time, reduces system both sides randomness, so as to improve system safe and stable operation effect Rate and Electricity Investment benefit.The trans-regional source lotus space-time complementary characteristic for building the statistical nature based on electric power big data assesses body System, including step (VI)-(Ⅸ)：

(VI) step 1) and the power generation characteristics and part throttle characteristics of different regions in step 2) are combed, considers to include differently Area's natural conditions, the energy structure difference external influence factors of regional disparity, in classification, assessment, prediction, correlation analysis, poly- On the basis of class, coordinate feature from source net lotus and regional complementarity carries out space-time complementation performance evaluation；

(VII) power supply complementary characteristic assess, by fluctuation to be contemplated, gas-to electricity hourage, regenerative resource permeability, The analysis and assessment of the dimensions such as regenerative resource output fraction；

(VIII) load complementary characteristic is assessed, by load fluctuation to be considered, kurtosis, season complementary characteristic, year complementary characteristic dimension The analysis and assessment of degree；

(Ⅸ) source lotus complementary characteristic is assessed, and is commented by regenerative resource accounting to be considered, peak-load regulating ability, complementary scale dimension Estimate analysis.

11. the source lotus complementary Benefit Evaluation Method spanning space-time of the consideration system bilateral randomness according to claim 1 or 10, It is characterized in that in (VI), natural conditions mainly include shadow of the factors such as weather and topography and geomorphology to generated output of renewable energy source Ring；Energy structure difference mainly includes the influences of the factor to electricity needs and part throttle characteristics such as the industrial structure, power mode；Area Difference mainly includes renewable energy source power condition, generation technology, and demand response, electric energy substitute multiplexe electric technology maturation Spend influences on the characteristic of power supply, load both sides；

The data for considering to count, calculate include substantial amounts of causality data, the space-time data of higher-dimension, the monitoring control of wide area System, quick time response and real-time control data, in addition to the state of power system, it is also necessary to obtain and analyze association area Data, and uncertainty during process part shortage of data, therefore theoretical using electric power big data leads to source lotus basic data Excessive data acquisition technology is aggregated into high in the clouds, is calculated by big data visualization technique, big data analysis and battle line technical Analysis each The complementary index of individual scheme, it is combined with multipotency stream complementation control technology, realizes the real-time optimization and reasonable disposition of energy resources；

Every complementary index in step (VII)-(VIII), correlation degree between two or more things is mainly described A kind of index, usual two or more things are complemented each other by certain contact, to improve overall efficacy, then claim it to have complementation Property, the complementarity, refer mainly between wind, light renewable energy power generation power, and different regions electric load in time domain and Complementarity spatially；

Traditional space-time is complementary, refers mainly in certain period, is distributed in two kinds or two kinds of areal in time The utilizability of regenerative resource above is different；Complementarity spatially refers mainly to one kind in synchronization, different location Or it is complementary possessed by various energy resources resource, complementation spanning space-time be on the basis of traditional space-time complementation, in regional space and Extended in time scale, break through national boundaries and time zone limitation meet global generation assets under global energy Background of Internet with The space-time complementary characteristic of load；

Regenerative resource has good space-time complementary in itself in wide scope, if having made full use of this complementarity, The intermittence and unstability that wind-powered electricity generation can effectively be alleviated negatively affect to caused by power network, can only be on daytime for solar energy Utilize, but by being networked across state, when the time difference of 8 hours between China and Europe can be utilized to extend the use of solar energy resources Between, while the load fluctuation in two areas can be stabilized, so as to reduce the randomness of source lotus both sides, operation benefit is improved,

Specific complementary evaluation method is weighed with coefficient correlation, and coefficient correlation is for measuring the association between two stochastic variables The amount of degree, more weak then its value of correlation degree are smaller, it is meant that complementary stronger, its calculation formula is：

<mrow> <msub> <mi>R</mi> <mrow> <mi>X</mi> <mi>Y</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>t</mi> </msub> <mo>-</mo> <msub> <mi>X</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mi>Y</mi> <mi>t</mi> </msub> <mo>-</mo> <msub> <mi>Y</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <msqrt> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>t</mi> </msub> <mo>-</mo> <msub> <mi>X</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <msqrt> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>Y</mi> <mi>t</mi> </msub> <mo>-</mo> <msub> <mi>Y</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>19</mn> <mo>)</mo> </mrow> </mrow>

In formula：R_XYFor coefficient correlation；X_t、Y_tRespectively two time serieses under the i of time scale interval；X₀、Y₀Respectively sequence Arrange X_t、Y_tAverage value,

The poor Δ P (t) of the adjacent time inter of certain time series, which is analyzed, can understand the ripple of this time sequence in more detail Emotionally condition, calculation formula are：

Δ P (t)=P (t)-P (t-1) (20)

P (t) is the power output or load under time scale t in formula,

Smoothing effect coefficient S：Based on the standard deviation of generated output sequence, normalized with installed capacity and define smoothing effect coefficient S,

Subscript " single " and " cluster " corresponding single game and the situation of cluster, the index are used to quantify cluster wind field phase in formula Smoothness for single game to scene fluctuation；

It is complementary in a steady stream in the step (VII)：Mainly for the solar energy of different regions, wind energy, water energy regenerative resource, according to The natural characteristic and changing rule of related energy resources, simulation different time scales, hour, season, the development of resources characteristic in year, Typical energy source forms of electricity generation is chosen, different time scales, hour, season, the renewable energy power generation characteristic in year are covered in foundation；Examine Consider the external factor such as different regions weather conditions, topography and geomorphology, grid-connected conditions, technology maturity, the system based on electric power big data Count characteristic simulation future different type energy power curve；Emphasis is from output fluctuation, gas-to electricity hourage, regenerative resource Its complementary characteristic of permeability, fraction etc.；

In the step (VIII), lotus lotus is complementary：Population, GDP levels, the industrial structure, energization ratio mainly for different regions, Per capita household electricity consumption factor, this area's future power consumption and peak load are predicted；In addition, according to local history power load Curve, with reference to the change of its industrial structure and power mode, electric load of the statistical nature based on electric power big data to future Curve is simulated, and grasps its different time scales, year, season, the moon, the maximum of day, minimum load and peak-valley difference information；Finally weigh Point carries out comprehensive analysis in terms of load fluctuation, kurtosis, season complementary and annual complementation；

In the step (Ⅸ), source lotus is complementary：On the basis of complementation, lotus lotus complementation analysis in a steady stream, renewable energy is considered as a whole Source is grid-connected with the mechanism that influences each other of local source net lotus, and the source lotus that analysis is contrasted between the different zones energy coordinates feature performance, fortune With table of the electric power big data correlation theory to interregional source lotus complementation in terms of regenerative resource permeability is improved, reduce peak-valley difference Now carry out integrated complementary evaluation.