CN104463378A - Algorithm for wind power generation absorption capacity of provincial power grid in area where hydropower resources are rich - Google Patents

Abstract

The invention belongs to the technical field of power grid dispatching computing, and particularly relates to an algorithm for the wind power generation absorption capacity of a provincial power grid in an area where hydropower resources are rich. The computing method starts from the dispatching and peak load regulation aspect of a provincial electric power system, and by predicting the newly-increased hydropower, thermal power and new energy power source condition in future, the wind and power coincidence factor is predicted through a Monte Carlo random-probability analysis method; the water and power grid-combined power generation output is predicted according to historical data and meteorological conditions; the wind power and hydropower combined probability distribution is calculated through historical data sampling, wind power and hydropower relevance is analyzed, and wind power and hydropower time sequence power output used for calculating the minimum start-up mode and the wind power absorbing capacity of the provincial power grid is obtained; on the basis, the minimum start-up mode of the power grid is subjected to optimized calculation, the peak load regulation capacity space of the power grid is obtained, and therefore the gird-combined wind power generation output absorbing capacity of the peak load regulation capacity space of the power grid is calculated all the year around in a rolling calculation mode, and the accurate wind curtailment amount of a system can be obtained all the year around in future.

Description

The algorithm of regional provincial power network wind-power electricity generation digestion capability is enriched for hydroelectric resources

Technical field

The invention belongs to dispatching of power netwoks computing technique field, particularly relate to a kind of algorithm enriching regional provincial power network wind-power electricity generation digestion capability for hydroelectric resources.

Background technology

In recent years, China's wind power generation industry develop rapidly, newly-increased wind-power electricity generation installed capacity 16088.7MW in 2013, increases by 24.1% on a year-on-year basis; Accumulative installed capacity 91412.89MW, increases by 21.4% on a year-on-year basis, and newly-increased installation and accumulative installation two item number are according to all ranking first in the world.The wind power-generating grid-connected electric system digestion capability that causes is not enough in a large number, and " abandoning wind limit wind " phenomenon often occurs, and cause wind-powered electricity generation to utilize the reduction of hourage, economic loss is huge, and within 2013, abandon air quantity and reach 15,000,000,000 kilowatt hours, economic loss is huge.Therefore study electrical network the receptivity of dissolving of wind-power electricity generation is more and more paid close attention to, particularly provincial and above region bulk power grid.

Moment must keep generating and coulomb balance during Operation of Electric Systems, otherwise the skew of frequency or voltage will be caused.After wind power-generating grid-connected, due to the stochastic uncertainty that it is exerted oneself, add the balance difficulty of systems generate electricity and electricity consumption, particularly when in system, the ratio of wind-powered electricity generation is higher, in system, other power supplys must be that wind-powered electricity generation is for subsequent use, when wind power output is large in the moment, other power supplys reduce exerts oneself, wind power output hour, other power supply increases are exerted oneself, to maintain system Real-time Balancing.

The dissolve calculating of wind-power electricity generation ability of electric system is the important foundation of Power System Planning and traffic control with assessment, needs from comprehensive analysis decisions in aspect such as power supply architecture, part throttle characteristics, ability to send outside, wind-powered electricity generation characteristic, market and economic means.Provincial power network comprises all kinds of different unit, and the installation of thermoelectricity, water power, wind-powered electricity generation dynamically increases, and be difficult to effectively predict peak load regulation network space, quantitative calculating provincial power network is a technical barrier for wind electricity digestion capability.Hydropower is as a kind of clean renewable energy power generation, and output power is comparatively stable, priority scheduling of should trying one's best; For the provincial power network that hydroelectric resources is abundant, sending out greatly the phase at abundance of water will affect dissolving of wind-powered electricity generation.Along with economic development, the particularly quick growth of tertiary industry proportion, and the raising of living standard of urban and rural population level, provincial power network peak-valley difference increases rapidly; The arid occurred in recent years especially and flood racing extreme climate, weaken the peak modulation capacity of same basin Hydropower Unit, electrical network generally adopts fired power generating unit start and stop and degree of depth peak regulation, hydroenergy storage station is drawn water, adjust the multiple means such as electricity of concluding the business between external electrical network contact gauze and carry out peak regulation.A large amount of wind power-generating grid-connected increase system reserve capacity, peaking power source is the necessary condition of balance wind-powered electricity generation fluctuation.When carrying out provincial power network scheduling, need the preferential region that the online, particularly hydroelectric resources of water power are abundant that ensures, the unadjustable property of water power can affect dissolving of wind-powered electricity generation, and peak load regulation network adds the fired power generating unit underrun time simultaneously.

Qualitative analysis aspect is mainly concentrated on for wind electricity digestion capability in current electrical network, the present situation that qualitative assessment computing method are less, particularly the quantitative calculating of the wind electricity digestion capability of provincial power network need to consider the traffic control of entire system and peak regulation for subsequent use, for the province that hydroelectric resources is abundant, the digestion capability of wind-powered electricity generation is subject to the impact of water power, how quantitative test difficulties.The present invention proposes a kind of peak load regulation network space that utilizes based on electrical network minimum load and minimum start-up mode and to dissolve the annual method for rolling computation of grid-connected wind-power electricity generation ability, water power is increased newly at the following provincial power network of accurately predicting, thermoelectricity and new forms of energy power conditions, accurately predicting wind-electricity integration installation scale and wind-powered electricity generation simultaneity factor, and on the basis of accurately predicting load growth situation, sampled by historical data, calculate wind-powered electricity generation water power joint probability distribution, analyze the correlativity of water power and wind-powered electricity generation, optimize the minimum start-up mode calculating electrical network, obtaining electrical network can peak space, the digestion capability of annual rolling calculation wind-power electricity generation, enrich hydroelectric resources area provincial power network in order to solve to comprise wind-power electricity generation is dissolved the qualitative assessment of receptivity and this technical barrier of computing method.

Summary of the invention

For the problems referred to above, the present invention proposes a kind of algorithm enriching regional provincial power network wind-power electricity generation digestion capability for hydroelectric resources, comprising:

Step 1, according to power source planning situation, the power supply installation of prediction following newly-increased water power, thermoelectricity and wind-powered electricity generation increases;

Step 2, to increase and the ancillary transmission transmission project construction period according to wind-powered electricity generation installation, prediction provincial power network wind-electricity integration installation scale;

Step 3, according to historical data and economic growth situation, prediction provincial power network load growth, obtains provincial power network minimum load; Prediction provincial power network dominant eigenvalues transmission fluctuation;

Step 4, according to Monte Carlo random chance analytical approach prediction wind-powered electricity generation simultaneity factor; Generate electricity by way of merging two or more grid systems according to historical data and meteorological conditional forecasting water power and exert oneself; Sampled by historical data, calculate wind-powered electricity generation water power joint probability distribution, analyze the correlativity of water power and wind-powered electricity generation, obtain the time series power stage for the water power and wind-powered electricity generation calculating the minimum start-up mode of provincial power network and wind electricity digestion capability;

Step 5, while meeting water power priority scheduling, optimize and calculate the minimum start-up mode of electrical network, calculate the variable capacity space of electrical network;

Step 6, rolling calculation electrical network variable capacity space are dissolved the ability of wind-power electricity generation, calculate annual to abandon air quantity.

In described step 4, the computing formula of wind-powered electricity generation water power joint probability distribution is as follows:

$f(x,y)=\frac{1}{{2\mathrm{\π\σ}}_x{\mathrm{\σ}}_y\sqrt{1-r^2}}{e}^{-\frac{1}{2(1-r^2)}[\frac{{(x-{\mathrm{\μ}}_x)}^2}{{{\mathrm{\σ}}_x}^2}-\frac{2r(x-{\mathrm{\μ}}_x)(y-{\mathrm{\μ}}_y)}{{\mathrm{\σ}}_x{\mathrm{\σ}}_y}+\frac{{(y-{\mathrm{\μ}}_y)}^2}{{{\mathrm{\σ}}_y}^2}]}$

Wherein, x is that water power is exerted oneself at random; Y is that wind-powered electricity generation is exerted oneself at random; μ _x, μ _y, σ _x, σ _y, r is distribution parameter, obtained by matching.

In described step 5, the optimization of the minimum start-up mode of electrical network calculates and comprises:

First set up objective function, make while meeting water power priority scheduling, by optimizing the start and stop of fired power generating unit and exerting oneself and make provincial power network burnup within a certain period of time minimum, objective function is as follows:

$\min \underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{u}_j\left(t\right)F\left(p_j\left(t\right)\right)+u_j\left(t\right)(1-u_j(t-1))s_j\left(t\right)$

Wherein, T is for optimizing time span, and N is fired power generating unit number in system, p _j(t) for fired power generating unit j is in the power output of time t, F (p _j(t)) be the burnup function of fired power generating unit; u _jt start and stop state that () is fired power generating unit; s _jt start and stop burnup that () is fired power generating unit;

The operation burnup of fired power generating unit fits to a quadratic function, as follows:

F(p _j(t))＝a _jp _j ²(t)+b _jp _j(t)+c _j

Wherein, a _j, b _j, c _jfor the burnup fraction of fired power generating unit j;

The startup burnup of fired power generating unit calculates with following exponential function:

$s_j^u\left(t\right)={\mathrm{\α}}_j+{\mathrm{\β}}_j[1-\exp (-X_j^{\mathrm{off}}\left(t\right)/{\mathrm{\τ}}_j)]$

Wherein, α _j, β _jfor unit starting consumption constant; for unit j is in oneself warp of the time t lasting time of stopping transport; τ _jfor constant cool time of boiler;

Then set up constraint condition, comprising:

A) grid power Constraints of Equilibrium: ensure that water power priority scheduling uses, the gross generation of water power and thermoelectricity must meet the demand of system total load,

$\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{u}_j\left(t\right)p_j\left(t\right)+p_w\left(t\right)=p_D\left(t\right)$

Wherein, p _wt () is exerted oneself for the grid-connected water power of electrical network, p _dt () is network load;

B) electrical network spinning reserve constraint: for ensureing reliable power supply and the good quality of power supply, maintain the reliability of system power supply, sufficient spinning reserve must be provided, namely within certain time period, if have unit outage or extraordinary maintenance, other unit must have enough margin capacities to supplement

$\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{u}_j\left(t\right){\stackrel{\‾}{P}}_j\left(t\right)+p_w\left(t\right)\≥p_D\left(t\right)+s_D\left(t\right)$

Wherein, for fired power generating unit maximum output; S _d(t) for electrical network for subsequent use;

C) the minimum start-off time constraints of fired power generating unit: for avoiding fired power generating unit start and stop frequently;

$\underset{n=t-T_{1j}}{\overset{t-1}{\mathrm{\Σ}}}{u}_j\left(n\right)\≥T_{1j}{u}_j(t-1)(1-u_j\left(t\right))$

$\underset{n=t-T_{2j}}{\overset{t-1}{\mathrm{\Σ}}}(1-u_j\left(n\right))\≥T_{2j}{u}_j\left(t\right)(1-u_j(t-1))$

Wherein, T _1jfor the fired power generating unit continuous on time; T _2jfor fired power generating unit continuous stop time;

D) fired power generating unit is gained merit units limits: because the meritorious certain limit of having exerted oneself of fired power generating unit, this is determined by unit self-characteristic,

${\underset{\‾}{P}}_j*u_j\left(t\right)\≤p_j\left(t\right)\≤{\stackrel{\‾}{P}}_j*u_j\left(t\right)$

Wherein, p _jfor thermal power unit operation minimum load;

E) Climing constant: because every platform fired power generating unit exerting oneself within the unit interval increases and reduce certain amplitude restriction, therefore will consider the climbing rate constraint of unit,

p _j(t)-p _j(t-1)≤Ru _j*u _j(t-1)

p _j(t-1)-p _j(t)≤Rd _j*u _j(t)

Wherein, Ru _jfor fired power generating unit is upwards climbed slope, Rd _jfor fired power generating unit is climbed downwards slope;

F) environmental constraints: the waste gas total amount of namely discharging should meet the requirement of environmental protection, can not exceed discharge permit,

$V_{\mathrm{CO}2}\underset{t}{\mathrm{\Σ}}\underset{j}{\mathrm{\Σ}}{u}_j\left(t\right)p_j\left(t\right)\≤H_{\mathrm{CO}2}$

Wherein, V _cO2for the CO2 discharge capacity that unit thermoelectricity generated energy produces, H _cO2for the restriction of CO2 total emission volumn;

The start and stop of fired power generating unit and situation of exerting oneself is calculated, therefore tracking thermoelectricity minimum load P finally by optimization _{t_min}(t) be:

$P_{t\_\min }\left(t\right)=\underset{j}{\mathrm{\Σ}}{u}_j\left(t\right)p_j\left(t\right).$

In described step 5, the computing formula in the variable capacity space of electrical network is:

P _con(t)＝P _L(t)-(P _{w_min}(t)+P _{t_min}(t)+P _e(t))

In formula: P _confor electrical network can peak space; P _lfor system loading, i.e. tracking bore; P _{w_min}for tracking water power minimum load; P _{t_min}for tracking thermoelectricity minimum load; P _efor interconnection power input; T is time step.

Described step 6 comprises:

First wind power generation output ability is calculated,

P _wind(t)＝P _{w_install}*K _w(t)

In formula: P _windfor wind power generation output ability; P _{w_install}for grid-connected wind-power electricity generation installed capacity; K _wfor wind-powered electricity generation simultaneity factor;

Then grid-connected wind power generation output is calculated,

If a) P _con(t) > 0, and P _con(t) > P _wind(t), then:

P _{wind_in}(t)＝P _wind(t)

If b) P _con(t) > 0, and P _con(t) < P _wind(t), then:

P _{wind_in}(t)＝P _con(t)

If c) P _con(t) < 0, then:

P _{wind_in}(t)＝0

In formula: P _{wind_in}t () is grid-connected wind power generation output;

Finally adopt by time the computing method of the rolling provincial power networks that calculate containing hydroelectric resources district galore abandon air quantity comparatively accurately the whole year, computing formula is as follows:

$C_{\mathrm{lost}}=\underset{t}{\mathrm{\&Sigma;}}[P_{\mathrm{wind}}\left(t\right)-P_{\mathrm{wind}\_\mathrm{in}}\left(t\right)]$

In formula: C _lostfor system abandons air quantity the whole year.

Beneficial effect of the present invention is: these computing method dispatch the angle with peak regulation from Provincial Electric Power System, at accurately predicting newly-increased water power in future, thermoelectricity installation increases, wind-powered electricity generation installation increases and wind power output situation, on the basis of load growth situation, propose a kind of based on wind and water combined distribution probability and the minimum start-up mode of provincial power network of correlation analysis and the optimized calculation method in variable capacity space, according to electrical network variable capacity space annual by time rolling calculation provincial power network wind electricity digestion capability, fill up the blank that provincial power network wind-power electricity generation digestion capability qualitative assessment calculates field.Meanwhile, the present invention can provide reference for the Real-Time Scheduling of grid-connected wind-power electricity generation and running optimizatin, reduces " abandoning wind limit wind " phenomenon of wind-power electricity generation, improves wind-powered electricity generation utilization factor and economy.

Accompanying drawing explanation

Fig. 1 is the algorithm flow chart enriching regional provincial power network wind-power electricity generation digestion capability for hydroelectric resources;

Fig. 2 is certain provincial power network at typical day peak regulation and wind electricity digestion situation and calculate the minimum start of electrical network and exert oneself and variable capacity space curve schematic diagram of wet season.

Embodiment

Below in conjunction with accompanying drawing, preferred embodiment is elaborated.

Enrich an algorithm for regional provincial power network wind-power electricity generation digestion capability for hydroelectric resources, as shown in Figure 1, comprising:

Step 1, according to power source planning situation, the power supply installation of prediction following newly-increased water power, thermoelectricity and wind-powered electricity generation increases;

Step 2, to increase and the ancillary transmission transmission project construction period according to wind-powered electricity generation installation, prediction provincial power network wind-electricity integration installation scale;

Step 3, according to historical data and economic growth situation, prediction provincial power network load growth, obtains provincial power network minimum load; Prediction provincial power network dominant eigenvalues transmission fluctuation;

Step 4, according to Monte Carlo random chance analytical approach prediction wind-powered electricity generation simultaneity factor; Generate electricity by way of merging two or more grid systems according to historical data and meteorological conditional forecasting water power and exert oneself; Sampled by historical data, calculate wind-powered electricity generation water power joint probability distribution, analyze the correlativity of water power and wind-powered electricity generation, obtain the time series power stage for the water power and wind-powered electricity generation calculating the minimum start-up mode of provincial power network and wind electricity digestion capability;

Step 5, while meeting water power priority scheduling, optimize and calculate the minimum start-up mode of electrical network, calculate the variable capacity space of electrical network;

Step 6, rolling calculation electrical network variable capacity space are dissolved the ability of wind-power electricity generation, calculate annual to abandon air quantity.

In described step 4, the computing formula of wind-powered electricity generation water power joint probability distribution is as follows:

$f(x,y)=\frac{1}{{2\mathrm{\&pi;\&sigma;}}_x{\mathrm{\&sigma;}}_y\sqrt{1-r^2}}{e}^{-\frac{1}{2(1-r^2)}[\frac{{(x-{\mathrm{\&mu;}}_x)}^2}{{{\mathrm{\&sigma;}}_x}^2}-\frac{2r(x-{\mathrm{\&mu;}}_x)(y-{\mathrm{\&mu;}}_y)}{{\mathrm{\&sigma;}}_x{\mathrm{\&sigma;}}_y}+\frac{{(y-{\mathrm{\&mu;}}_y)}^2}{{{\mathrm{\&sigma;}}_y}^2}]}$

Wherein, x is that water power is exerted oneself at random; Y is that wind-powered electricity generation is exerted oneself at random; μ _x, μ _y, σ _x, σ _y, r is distribution parameter, obtained by matching.

In described step 5, the optimization of the minimum start-up mode of electrical network calculates and comprises:

First set up objective function, make while meeting water power priority scheduling, by optimizing the start and stop of fired power generating unit and exerting oneself and make provincial power network burnup within a certain period of time minimum, objective function is as follows:

$\min \underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_j\left(t\right)F\left(p_j\left(t\right)\right)+u_j\left(t\right)(1-u_j(t-1))s_j\left(t\right)$

Wherein, T is for optimizing time span, and N is fired power generating unit number in system, p _j(t) for fired power generating unit j is in the power output of time t, F (p _j(t)) be the burnup function of fired power generating unit; u _jt start and stop state that () is fired power generating unit; s _jt start and stop burnup that () is fired power generating unit;

The operation burnup of fired power generating unit fits to a quadratic function, as follows:

F(p _j(t))＝a _jp _j ²(t)+b _jp _j(t)+c _j

Wherein, a _j, b _j, c _jfor the burnup fraction of fired power generating unit j;

The startup burnup of fired power generating unit calculates with following exponential function:

$s_j^u\left(t\right)={\mathrm{\&alpha;}}_j+{\mathrm{\&beta;}}_j[1-\exp (-X_j^{\mathrm{off}}\left(t\right)/{\mathrm{\&tau;}}_j)]$

Wherein, α _j, β _jfor unit starting consumption constant; for unit j is in oneself warp of the time t lasting time of stopping transport; τ _jfor constant cool time of boiler;

Then set up constraint condition, comprising:

A) grid power Constraints of Equilibrium: ensure that water power priority scheduling uses, the gross generation of water power and thermoelectricity must meet the demand of system total load,

$\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_j\left(t\right)p_j\left(t\right)+p_w\left(t\right)=p_D\left(t\right)$

Wherein, p _wt () is exerted oneself for the grid-connected water power of electrical network, p _dt () is network load;

B) electrical network spinning reserve constraint: for ensureing reliable power supply and the good quality of power supply, maintain the reliability of system power supply, sufficient spinning reserve must be provided, namely within certain time period, if have unit outage or extraordinary maintenance, other unit must have enough margin capacities to supplement

$\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_j\left(t\right){\stackrel{\&OverBar;}{P}}_j\left(t\right)+p_w\left(t\right)\&GreaterEqual;p_D\left(t\right)+s_D\left(t\right)$

Wherein, for fired power generating unit maximum output; S _d(t) for electrical network for subsequent use;

C) the minimum start-off time constraints of fired power generating unit: for avoiding fired power generating unit start and stop frequently;

$\underset{n=t-T_{1j}}{\overset{t-1}{\mathrm{\&Sigma;}}}{u}_j\left(n\right)\&GreaterEqual;T_{1j}{u}_j(t-1)(1-u_j\left(t\right))$

$\underset{n=t-T_{2j}}{\overset{t-1}{\mathrm{\&Sigma;}}}(1-u_j\left(n\right))\&GreaterEqual;T_{2j}{u}_j\left(t\right)(1-u_j(t-1))$

Wherein, T _1jfor the fired power generating unit continuous on time; T _2jfor fired power generating unit continuous stop time;

D) fired power generating unit is gained merit units limits: because the meritorious certain limit of having exerted oneself of fired power generating unit, this is determined by unit self-characteristic,

${\underset{\&OverBar;}{P}}_j*u_j\left(t\right)\&le;p_j\left(t\right)\&le;{\stackrel{\&OverBar;}{P}}_j*u_j\left(t\right)$

Wherein, p _jfor thermal power unit operation minimum load;

E) Climing constant: because every platform fired power generating unit exerting oneself within the unit interval increases and reduce certain amplitude restriction, therefore will consider the climbing rate constraint of unit,

p _j(t)-p _j(t-1)≤Ru _j*u _j(t-1)

p _j(t-1)-p _j(t)≤Rd _j*u _j(t)

Wherein, Ru _jfor fired power generating unit is upwards climbed slope, Rd _jfor fired power generating unit is climbed downwards slope;

F) environmental constraints: the waste gas total amount of namely discharging should meet the requirement of environmental protection, can not exceed discharge permit,

$V_{\mathrm{CO}2}\underset{t}{\mathrm{\&Sigma;}}\underset{j}{\mathrm{\&Sigma;}}{u}_j\left(t\right)p_j\left(t\right)\&le;H_{\mathrm{CO}2}$

Wherein, V _cO2for the CO2 discharge capacity that unit thermoelectricity generated energy produces, H _cO2for the restriction of CO2 total emission volumn;

The start and stop of fired power generating unit and situation of exerting oneself is calculated, therefore tracking thermoelectricity minimum load P finally by optimization _{t_min}(t) be:

$P_{t\_\min }\left(t\right)=\underset{j}{\mathrm{\&Sigma;}}{u}_j\left(t\right)p_j\left(t\right).$

In described step 5, the computing formula in the variable capacity space of electrical network is:

P _con(t)＝P _L(t)-(P _{w_min}(t)+P _{t_min}(t)+P _e(t))

In formula: P _confor electrical network can peak space; P _lfor system loading, i.e. tracking bore; P _{w_min}for tracking water power minimum load; P _{t_min}for tracking thermoelectricity minimum load; P _efor interconnection power input; T is time step.

Described step 6 comprises:

First wind power generation output ability is calculated,

P _wind(t)＝P _{w_install}*K _w(t)

In formula: P _windfor wind power generation output ability; P _{w_install}for grid-connected wind-power electricity generation installed capacity; K _wfor wind-powered electricity generation simultaneity factor;

Then grid-connected wind power generation output is calculated,

If a) P _con(t) > 0, and P _con(t) > P _wind(t), then:

P _{wind_in}(t)＝P _wind(t)

If b) P _con(t) > 0, and P _con(t) < P _wind(t), then:

P _{wind_in}(t)＝P _con(t)

If c) P _con(t) < 0, then:

P _{wind_in}(t)＝0

In formula: P _{wind_in}t () is grid-connected wind power generation output;

Finally adopt by time the computing method of the rolling provincial power networks that calculate containing hydroelectric resources district galore abandon air quantity comparatively accurately the whole year, computing formula is as follows:

$C_{\mathrm{lost}}=\underset{t}{\mathrm{\&Sigma;}}[P_{\mathrm{wind}}\left(t\right)-P_{\mathrm{wind}\_\mathrm{in}}\left(t\right)]$

In formula: C _lostfor system abandons air quantity the whole year.

Embodiment

Certain provincial power network the wet season typical day peak regulation and wind electricity digestion situation as shown in Figure 2: according to thermoelectricity, water power, wind-powered electricity generation installation growth pattern, grid connected wind power growth pattern, the load growth situation of prediction and interconnection tie power fluctuation situation between economizing, wind-powered electricity generation simultaneity factor is predicted by Monte Carlo random chance analytical approach, sampled by historical data, calculate wind and water combined probability distribution, analyze the correlativity of water power and wind-powered electricity generation, obtain the time series power stage of water power and wind-powered electricity generation; By optimize calculate the minimum start of electrical network exert oneself and variable capacity space as shown in curve in Fig. 2, typical case day abandon air quantity as shown in black region in Fig. 2, abandoning air quantity the year that rolling calculation obtains is 0.336 hundred million kilowatt hours.

The above; be only the present invention's preferably embodiment, but protection scope of the present invention is not limited thereto, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; the change that can expect easily or replacement, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.

Claims (5)

1. enrich an algorithm for regional provincial power network wind-power electricity generation digestion capability for hydroelectric resources, it is characterized in that, comprising:

Step 1, according to power source planning situation, the power supply installation of prediction following newly-increased water power, thermoelectricity and wind-powered electricity generation increases;

Step 2, to increase and the ancillary transmission transmission project construction period according to wind-powered electricity generation installation, prediction provincial power network wind-electricity integration installation scale;

Step 3, according to historical data and economic growth situation, prediction provincial power network load growth, obtains provincial power network minimum load; Prediction provincial power network dominant eigenvalues transmission fluctuation;

Step 4, according to Monte Carlo random chance analytical approach prediction wind-powered electricity generation simultaneity factor; Generate electricity by way of merging two or more grid systems according to historical data and meteorological conditional forecasting water power and exert oneself; Sampled by historical data, calculate wind-powered electricity generation water power joint probability distribution, analyze the correlativity of water power and wind-powered electricity generation, obtain the time series power stage for the water power and wind-powered electricity generation calculating the minimum start-up mode of provincial power network and wind electricity digestion capability;

Step 5, while meeting water power priority scheduling, optimize and calculate the minimum start-up mode of electrical network, calculate the variable capacity space of electrical network;

Step 6, rolling calculation electrical network variable capacity space are dissolved the ability of wind-power electricity generation, calculate annual to abandon air quantity.

2. algorithm according to claim 1, it is characterized in that, in described step 4, the computing formula of wind-powered electricity generation water power joint probability distribution is as follows:

$f(x,y)=\frac{1}{2\mathrm{\&pi;}{\mathrm{\&sigma;}}_x{\mathrm{\&sigma;}}_y\sqrt{1-r^2}}{e}^{-\frac{1}{2(1-r^2)}[\frac{{(x-{\mathrm{\&mu;}}_x)}^2}{{{\mathrm{\&sigma;}}_x}^2}-\frac{2r(x-{\mathrm{\&mu;}}_x)(y-{\mathrm{\&mu;}}_y)}{{\mathrm{\&sigma;}}_x{\mathrm{\&sigma;}}_y}+\frac{{(y-{\mathrm{\&mu;}}_y)}^2}{{{\mathrm{\&sigma;}}_y}^2}]}$

Wherein, x is that water power is exerted oneself at random; Y is that wind-powered electricity generation is exerted oneself at random; μ _x, μ _y, σ _x, σ _y, r is distribution parameter, obtained by matching.

3. algorithm according to claim 1, is characterized in that, in described step 5, the optimization of the minimum start-up mode of electrical network calculates and comprises:

First set up objective function, make while meeting water power priority scheduling, by optimizing the start and stop of fired power generating unit and exerting oneself and make provincial power network burnup within a certain period of time minimum, objective function is as follows:

$\min \underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_j\left(t\right)F\left(p_j\left(t\right)\right)+u_j\left(t\right)(1-u_j(t-1))s_j\left(t\right)$

Wherein, T is for optimizing time span, and N is fired power generating unit number in system, p _j(t) for fired power generating unit j is in the power output of time t, F (p _j(t)) be the burnup function of fired power generating unit; u _jt start and stop state that () is fired power generating unit; s _jt start and stop burnup that () is fired power generating unit;

The operation burnup of fired power generating unit fits to a quadratic function, as follows:

F(p _j(t))＝a _jp _j ²(t)+b _jp _j(t)+c _j

Wherein, a _j, b _j, c _jfor the burnup fraction of fired power generating unit j;

The startup burnup of fired power generating unit calculates with following exponential function:

$s_j^u\left(t\right){=\mathrm{\&alpha;}}_j+{\mathrm{\&beta;}}_j[1-\exp (-X_j^{\mathrm{off}}\left(t\right)/{\mathrm{\&tau;}}_j)]$

Wherein, α _j, β _jfor unit starting consumption constant; for unit j is in oneself warp of the time t lasting time of stopping transport; τ _jfor constant cool time of boiler;

Then set up constraint condition, comprising:

A) grid power Constraints of Equilibrium: ensure that water power priority scheduling uses, the gross generation of water power and thermoelectricity must meet the demand of system total load,

$\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_j\left(t\right)p_j\left(t\right)+p_w\left(t\right)=p_D\left(t\right)$

Wherein, p _wt () is exerted oneself for the grid-connected water power of electrical network, p _dt () is network load;

B) electrical network spinning reserve constraint: for ensureing reliable power supply and the good quality of power supply, maintain the reliability of system power supply, sufficient spinning reserve must be provided, namely within certain time period, if have unit outage or extraordinary maintenance, other unit must have enough margin capacities to supplement

$\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_j\left(t\right){\stackrel{\&OverBar;}{P}}_j\left(t\right)+p_w\left(t\right)\&GreaterEqual;p_D\left(t\right)+s_D\left(t\right)$

Wherein, for fired power generating unit maximum output; S _d(t) for electrical network for subsequent use;

C) the minimum start-off time constraints of fired power generating unit: for avoiding fired power generating unit start and stop frequently;

$\underset{n=t-T_{1j}}{\overset{t-1}{\mathrm{\&Sigma;}}}{u}_j\left(n\right)\&GreaterEqual;T_{1j}{u}_j(t-1)(1-u_j\left(t\right))$

$\underset{n=t-T_{2j}}{\overset{t-1}{\mathrm{\&Sigma;}}}(1-u_j\left(n\right))\&GreaterEqual;T_{2j}{u}_j\left(t\right)(1-u_j(t-1))$

Wherein, T _1jfor the fired power generating unit continuous on time; T _2jfor fired power generating unit continuous stop time;

D) fired power generating unit is gained merit units limits: because the meritorious certain limit of having exerted oneself of fired power generating unit, this is determined by unit self-characteristic,

${\underset{\&OverBar;}{P}}_j*u_j\left(t\right)\&le;p_j\left(t\right)\&le;{\stackrel{\&OverBar;}{P}}_j*u_j\left(t\right)$

Wherein, for thermal power unit operation minimum load;

E) Climing constant: because every platform fired power generating unit exerting oneself within the unit interval increases and reduce certain amplitude restriction, therefore will consider the climbing rate constraint of unit,

p _j(t)-p _j(t-1)≤Ru _j*u _j(t-1)

p _j(t-1)-p _j(t)≤Rd _j*u _j(t)

Wherein, Ru _jfor fired power generating unit is upwards climbed slope, Rd _jfor fired power generating unit is climbed downwards slope;

F) environmental constraints: the waste gas total amount of namely discharging should meet the requirement of environmental protection, can not exceed discharge permit,

$V_{\mathrm{CO}2}\underset{t}{\mathrm{\&Sigma;}}\underset{j}{\mathrm{\&Sigma;}}{u}_j\left(t\right)p_j\left(t\right)\&le;H_{\mathrm{CO}2}$

Wherein, V _cO2for the CO2 discharge capacity that unit thermoelectricity generated energy produces, H _cO2for the restriction of CO2 total emission volumn;

The start and stop of fired power generating unit and situation of exerting oneself is calculated, therefore tracking thermoelectricity minimum load P finally by optimization _{t_min}(t) be:

$P_{t\_\min }\left(t\right)=\underset{j}{\mathrm{\&Sigma;}}{u}_j\left(t\right)p_j\left(t\right).$

4. algorithm according to claim 1, it is characterized in that, in described step 5, the computing formula in the variable capacity space of electrical network is:

P _con(t)＝P _L(t)-(P _{w_min}(t)+P _{t_min}(t)+P _e(t))

In formula: P _confor electrical network can peak space; P _lfor system loading, i.e. tracking bore; P _{w_min}for tracking water power minimum load; P _{t_min}for tracking thermoelectricity minimum load; P _efor interconnection power input; T is time step.

5. algorithm according to claim 1, it is characterized in that, described step 6 comprises:

First wind power generation output ability is calculated,

P _wind(t)＝P _{w_install}*K _w(t)

In formula: P _windfor wind power generation output ability; P _{w_install}for grid-connected wind-power electricity generation installed capacity; K _wfor wind-powered electricity generation simultaneity factor;

Then grid-connected wind power generation output is calculated,

If a) P _con(t) > 0, and P _con(t) > P _wind(t), then:

P _{wind_in}(t)＝P _wind(t)

If b) P _con(t) > 0, and P _con(t) < P _wind(t), then:

P _{wind_in}(t)＝P _con(t)

If c) P _con(t) < 0, then:

P _{wind_in}(t)＝0

In formula: P _{wind_in}t () is grid-connected wind power generation output;

Finally adopt by time the computing method of the rolling provincial power networks that calculate containing hydroelectric resources district galore abandon air quantity comparatively accurately the whole year, computing formula is as follows:

$C_{\mathrm{lost}}=\underset{t}{\mathrm{\&Sigma;}}[P_{\mathrm{wind}}\left(t\right)-P_{\mathrm{wind}\_\mathrm{in}}\left(t\right)]$

In formula: C _lostfor system abandons air quantity the whole year.