CN104299173A - Robust optimization day-ahead scheduling method suitable for multi-energy-source connection - Google Patents

Abstract

The invention relates to a robust optimization day-ahead scheduling method suitable for multi-energy-source connection. The robust optimization day-ahead scheduling method includes the steps of firstly obtaining day-ahead power generation plans and unit parameters of a thermal power generation unit and a hydropower unit, established by an energy-saving power generation scheduling system, of provincial power company scheduling; obtaining short-period predication values of loads, generated wind power, generated small hydropower, generated photovoltaic power, generated gas power and the like, and setting a plurality of output scenes according to a certain proportion of inspective installed capacities of various renewable energy source power generation powers; building robust optimization day-ahead scheduling constraint conditions; building a robust optimization day-ahead scheduling model with the smallest sum of fuel cost and power generation cost of various renewable energy sources of the inner-adjustment thermal power generation unit in 96 time periods of a day as the optimization objective; solving the robust optimization day-ahead scheduling model with an artificial intelligent optimization algorithm, carrying out optimizing to obtain a globally-optimal solution, and correcting the day-ahead power generation plan of the thermal power generation unit of provincial power company scheduling of the energy-saving power generation scheduling system. By means of the robust optimization day-ahead scheduling method, scene processing methods of the power generation power of the various renewable energy sources are simplified, and the invalid reserve capacity and the redundancy reserve capacity are effectively avoided.

Description

A kind of robust being applicable to various energy resources access optimizes dispatching method a few days ago

Technical field

The present invention relates to electric system and automatic field, especially relate to a kind of robust being applicable to various energy resources access and optimize dispatching method a few days ago.

Background technology

The renewable energy power generation such as wind-force, photovoltaic, small hydropower system, combustion gas has the advantage of clean environment firendly, and the permeability in actual electric network is more and more higher.But the renewable energy power generations such as wind/light/water/gas have randomness, intermittence, and the prediction of its generated output exists comparatively big error, add the uncertainty that grid-connected rear operation plan is formulated.

Traditional generation schedule a few days ago dispatched according to the establishment such as prediction load, unit generation and turnaround plan, Tie line Power plan, unit consumption characteristic next day is one of core content of Economic Dispatch.After considering the renewable energy power generation large-scale grid connection such as wind/light/water/gas, traditional dispatching method a few days ago have ignored these regenerative resources and to exert oneself probabilistic impact, original determinacy a few days ago dispatching method will be no longer applicable, find new dispatching method a few days ago and seem particularly important, can be related to electric system economic security runs.

Summary of the invention

After the present invention mainly solves the renewable energy power generation large-scale grid connection such as wind/light/water/gas, its randomness of exerting oneself, intermittent to electrical network a few days ago operation plan formulate the uncertain problem brought.For the electric system containing renewable energy power generations such as wind/light/water/gas, propose a kind of robust being applicable to various energy resources access and optimize dispatching method a few days ago.On a few days ago middle basis of adjusting fired power generating unit unit commitment and the plan of exerting oneself of energy-saving power generation dispatching system formulates; do not change the start and stop arrangement of unit; centering adjusts exerting oneself of fired power generating unit suitably to revise, to meet the access demand of the renewable energy power generations such as wind/light/water/gas.

Consider randomness and the intermittence of renewable energy power generation, the process of scene is carried out according to the short-term forecasting value of renewable energy power generation power, optimize the unit output of robust running orbit to adapt to all scenes of exerting oneself, and exert oneself with for subsequent use by what adjust fired power generating unit in coordination optimization, effectively avoid invalid margin capacity and redundancy margin capacity.

Above-mentioned technical matters of the present invention is mainly solved by following technical proposals:

A kind of robust being applicable to various energy resources access optimizes dispatching method a few days ago, it is characterized in that, the objective function a few days ago dispatched based on the robust optimization of energy-saving power generation dispatching system and the robust Optimized Operation constraint condition of the access of the various energy resources such as wind/light/water/gas is applicable to based on one, this objective function is minimum for target with the generating expense summation of the fuel cost of one day 96 period fired power generating unit and various regenerative resource, and formula is as follows:

That is:

$\min \left\{\underset{s\∈X_j}{\max }\underset{\underset{\mathrm{\Ω}(\mathrm{\α},X_j)}{s}}{\min }\right\{\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{f}_i[p_{\mathrm{it}}+q_{\mathrm{it}}\left(s\right)]{\mathrm{\α}}_{\mathrm{it}}+\mathrm{\Σ\Σ}{C}_{\mathrm{\λ}}{P}_{\mathrm{\λ}}\left\}\right\}$ Formula one

Wherein: s is certain scene specific; X _jthe column vector of corresponding renewable energy power generation power residing scene under 96 periods; T is period sequence number, t=1,2, K, T; α _itfor the running status of unit i t period, value 0 or 1,0 represents shuts down, 1 representative start; p _itfor the plan on fired power generating unit i t period robust track is exerted oneself; q _its () is exerted oneself for the adjustment of fired power generating unit i t period under scene s; N _gfor fired power generating unit number of units; C _λfor the cost that other new forms of energy specific powers such as photovoltaic, combustion gas export; P _λfor other new forms of energy such as photovoltaic, combustion gas export general power;

Wherein:

$f_i\left(p_{\mathrm{it}}\right)=a_i{p}_{\mathrm{it}}^2+b_i{p}_{\mathrm{it}}+c_i$ Formula two

A _i, b _i, c _ibe respectively the energy consumption quadratic term of unit i, Monomial coefficient and constant term coefficient;

Described robust Optimized Operation constraint condition set up the demand of system reserve based on multiple energy/source accesses such as quantification wind/light/aqueous vapors, is divided into equality constraint and inequality constrain condition; Respectively:

Equality constraint one: active power balance under consideration new forms of energy predicated error, formula is as follows:

$\left\{\begin{array}{cc}\begin{array}{c}\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}^0=\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}[p_{\mathrm{it}}+q_{\mathrm{it}}\left(s^{\′}\right)]+{\mathrm{\ΔP}}_{\mathrm{it}}^{\′}\\ \underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}^0=\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}[p_{\mathrm{it}}+q_{\mathrm{it}}\left(s^{\′\′}\right)]+{\mathrm{\ΔP}}_{\mathrm{it}}^{\′\′}\\ \underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}^0=\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}+\mathrm{\Δ}\stackrel{\‾}{P_t}\end{array}& \∀t\end{array}\right.$ Formula three

In formula: exert oneself for adjusting the plan of fired power generating unit in the t period that energy-saving power generation dispatching system provides; p _itexert oneself for the unit i t period is meritorious; q _it(s') under scene s', the adjustment of fired power generating unit i t period is adjusted to exert oneself, q _it(s ") is scene s " descend the adjustment of middle tune fired power generating unit i t period to exert oneself; S', s " residing for current scene s up-and-down boundary scene (s "≤s≤s'); Δ P ' _itfor the renewable energy power generation power prediction error of t period under scene s', Δ P " _it" the renewable energy power generation power prediction error of lower t period for scene s; it is the renewable energy power generation power prediction AME of t period;

Inequality constrain condition one: middle tune fired power generating unit day part adjusts force constraint, and formula is as follows:

$\left\{\begin{array}{cc}\begin{array}{c}-\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{dn}}\≤q_{\mathrm{it}}\left(s^{\′}\right)\≤\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{up}}\\ -\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{dn}}\≤q_{\mathrm{it}}\left(s^{\′\′}\right)\≤\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{up}}\end{array}& \∀t,\∀i\end{array}\right.$ Formula four

In formula: Δ p _{i, up}, Δ p _{i, dn}be respectively unit i exert oneself rise, lower maximum rate; Δ t is the middle tune fired power generating unit spinning reserve response time, is set to 5min;

Inequality constrain condition two: middle tune fired power generating unit exert oneself bound constraint, formula is as follows:

$\left\{\begin{array}{cc}\begin{array}{c}{\mathrm{\α}}_{\mathrm{it}}{p}_{i\min }\≤p_{\mathrm{it}}+q_{\mathrm{it}}\left(s^{\′}\right)\≤{\mathrm{\α}}_{\mathrm{it}}{p}_{i\max }\\ {\mathrm{\α}}_{\mathrm{it}}{p}_{i\min }\≤p_{\mathrm{it}}+q_{\mathrm{it}}\left(s^{\′\′}\right)\≤{\mathrm{\α}}_{\mathrm{it}}{p}_{i\max }\end{array}& \∀t,\∀i\end{array}\right.$ Formula five

In formula: p _imax, p _iminbe respectively unit i technology to exert oneself upper and lower limit;

Inequality constrain condition three: on robust track, unit output range constraint, formula is as follows:

$\max (p_{i\min },0.9p_{\mathrm{it}}^0){\mathrm{\α}}_{\mathrm{it}}\≤p_{\mathrm{it}}\≤\min (p_{i\max },1.1p_{\mathrm{it}}^0){\mathrm{\α}}_{\mathrm{it}}$ Formula six

This constraint considers that correction is planned a few days ago should be not excessive with original departure degree of planning a few days ago;

This robust being applicable to various energy resources access is optimized dispatching method a few days ago and is comprised the following steps:

Step 1, obtains the middle tune fired power generating unit of energy-saving power generation dispatching system formulates and generation schedule a few days ago, the unit parameter of Hydropower Unit; According to energy-conservation, economic principle, priority scheduling Hydropower Unit, does not change the generation schedule a few days ago of Hydropower Unit;

Step 2, obtains the short-term forecasting value of wind-powered electricity generation and small power station, photovoltaic, fuel gas generation etc., by various renewable energy power generation power by the some scenes of exerting oneself of the setting ratio setting of respective installed capacity;

Step 3, adopt particle swarm optimization algorithm robust Optimal Operation Model, optimizing obtains globally optimal solution, and the plan namely obtained under each unit robust track is exerted oneself p _itto exert oneself q with adjustment _it(s'), q _it(s "); In searching process, initially exerting oneself of unit is arranged at random in the interval that formula six is determined, iteration convergence condition be the knots modification of global optimum's particle adaptive value continuous K time within the scope of convergence precision, iteration convergence precision setting is 0.001, wherein, K gets the positive integer being more than or equal to 20.

Dispatching method is a few days ago optimized at above-mentioned a kind of robust being applicable to various energy resources access, in described step 2, obtain the short-term forecasting value of wind-powered electricity generation and small power station, photovoltaic, fuel gas generation etc., various renewable energy power generation power is arranged some scenes of exerting oneself by the certain proportion of respective installed capacity;

According to the renewable energy power generation power short-term forecasting value of 96 periods, respectively the interval residing for various renewable energy power generation power is judged to each period, and then the scene under determining each period residing for various renewable energy power generation power; Finally, the matrix of the various renewable energy power generation power of reflection corresponding different scene under Different periods can be formed:

[X ₁, X ₂..., X _j, X _n] formula seven

Wherein, X ₁the column vector of corresponding wind-powered electricity generation residing scene under 96 periods, X _jrepresent that other regenerative resources are as small power station, photovoltaic, combustion gas.

Tool of the present invention has the following advantages: the scene process method simplifying various renewable energy power generation power; The robust Optimization Scheduling carried is minimum for optimization aim with the summation of the fuel cost of one day 96 period fired power generating unit and various renewable energy power generation expense, under meeting certain equality constraint and inequality constrain condition, exert oneself with for subsequent use by what adjust fired power generating unit in coordination optimization, effectively avoid invalid margin capacity and redundancy margin capacity.

Accompanying drawing explanation

Accompanying drawing 1 is wind energy turbine set 24 hours prediction curves of exerting oneself in embodiments of the invention.

Accompanying drawing 2 is small hydropower system 24 hours prediction curves of exerting oneself in embodiments of the invention.

Accompanying drawing 3 is wind power prediction scene schematic diagram.

Accompanying drawing 4 is principle of work schematic diagram of the present invention.

Accompanying drawing 5 is the parameter informations a few days ago planning fired power generating unit.

Accompanying drawing 6 is that in example, total cost of electricity-generating result is dispatched in robust optimization a few days ago.

Accompanying drawing 7 is that in example, certain Power Plant #1 robust optimizes scheduling result a few days ago.

Accompanying drawing 8 is in the regenerative resource installation total volume situation of setting, the result positive and negative for subsequent use of program computation.

Embodiment

Below by example, and by reference to the accompanying drawings, technical scheme of the present invention is described in further detail.

The present invention includes following steps:

Step 1, obtains the middle tune fired power generating unit of energy-saving power generation dispatching system formulates and generation schedule a few days ago, the unit parameter of Hydropower Unit.

The middle tune fired power generating unit that in acquisition embodiment, energy-saving power generation dispatching system is given and generation schedule a few days ago, the unit parameter of Hydropower Unit.Be embodiment unit parameter information as shown in Figure 5, comprise energy consumption function quadratic term coefficient, Monomial coefficient, constant term, unit maximum output, minimum load, upper creep speed and lower creep speed.

Step 2, obtains the short-term forecasting value of wind-powered electricity generation and small power station, photovoltaic, fuel gas generation etc., various renewable energy power generation power is arranged some scenes of exerting oneself by the certain proportion of respective installed capacity.

Obtain the short-term forecasting value of load in embodiment, small power station and wind power, photovoltaic, combustion gas etc.According to the renewable energy power generation power short-term forecasting value of 96 periods, respectively the interval residing for various renewable energy power generation power is judged to each period, and then the scene under determining each period residing for various renewable energy power generation power.Finally, the matrix of the various renewable energy power generation power of reflection corresponding different scene under Different periods can be formed:

[X ₁,X ₂，L,X _n] (8)

Wherein, X ₁the column vector of corresponding wind-powered electricity generation residing scene under 96 periods, X ₂, L, X _nrepresent that other regenerative resources are as small power station, photovoltaic, combustion gas.

In the present embodiment,

The installed capacity of photovoltaic and fuel gas generation is much smaller than the installed capacity of wind-powered electricity generation and small hydropower system.For the prediction curve of exerting oneself of wind-powered electricity generation and small hydropower system, Fig. 1 is wind energy turbine set 24 hours prediction curves of exerting oneself in embodiment, and wherein the installed capacity of wind energy turbine set 1 is 1300MW, and the installed capacity of wind energy turbine set 2 is 1100MW.Fig. 2 is small hydropower system 24 hours prediction curves of exerting oneself in embodiment, and wherein the installation of small power station 1, small power station 2 is 1000MW.

Respectively with wind-powered electricity generation, small hydropower system generated output be its separately installed capacity 20%, 40%, 60%, 80%, 100% for according to arranging 5 kinds of scenes.

The scene setting of exerting oneself of wind-powered electricity generation, small power station, photovoltaic, combustion gas should be noted the selection of scene interval, if the 4th period prediction wind power in accompanying drawing 3 is between scene 3 and scene 4, then the wind power output scene coboundary of the 4th period is considered with scene 4, and scene lower boundary is considered with scene 3.In like manner, the 3rd period prediction wind power is just in scene 3, then the wind power output scene coboundary of the 3rd period is considered with scene 4, and scene lower boundary is considered with scene 3.

Step 3, according to the classical Forecasting Methodology of electric load, obtains Short Term Load value.Load forecast is carried out as adopted least square method in embodiment.

Step 4, quantizes the demand of the various energy resources accesses such as wind/light/water/gas to system reserve, sets up robust Optimized Operation constraint condition, be divided into equality constraint and inequality constrain condition.

In embodiment, to consider wind power prediction scene, set up robust Optimized Operation constraint condition, specifically comprise following constraint:

1) in adjust fired power generating unit exert oneself summation adjustment before and after equal:

$\left\{\begin{array}{cc}\begin{array}{c}\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}^0=\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}[p_{\mathrm{it}}+q_{\mathrm{it}}\left(s^{\′}\right)]+{\mathrm{\ΔP}}_{\mathrm{it}}^{\′}\\ \underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}^0=\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}[p_{\mathrm{it}}+q_{\mathrm{it}}(s^{\′\′}]+{\mathrm{\ΔP}}_{\mathrm{it}}^{\′\′}\\ \underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}^0=\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}+\mathrm{\Δ}\stackrel{\‾}{P_t}\end{array}& \∀t\end{array}\right.---\left(9\right)$

In formula: exert oneself for adjusting the plan of fired power generating unit in the t period that energy-saving power generation dispatching system provides; p _itexert oneself for the unit i t period is meritorious; q _it(s') under scene s', the adjustment of fired power generating unit i t period is adjusted to exert oneself, q _it(s ") is scene s " descend the adjustment of middle tune fired power generating unit i t period to exert oneself; S', s " residing for current scene s up-and-down boundary scene (s "≤s≤s'); Δ P ' _itfor the renewable energy power generation power prediction error of t period under scene s', Δ P " _it" the renewable energy power generation power prediction error of lower t period for scene s; it is the renewable energy power generation power prediction AME of t period.

2) fired power generating unit day part is adjusted to adjust force constraint in:

$\left\{\begin{array}{cc}\begin{array}{c}-\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{dn}}\≤q_{\mathrm{it}}\left(s^{\′}\right)\≤\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{up}}\\ -\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{dn}}\≤q_{\mathrm{it}}\left(s^{\′\′}\right)\≤\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{up}}\end{array}& \∀t,\∀i\end{array}\right.---\left(10\right)$

In formula: Δ _{pi, up}, Δ _{pi, dn}be respectively unit i exert oneself rise, lower maximum rate; Δ t is the middle tune fired power generating unit spinning reserve response time, is set to 5min.

3) in adjust fired power generating unit exert oneself bound constraint:

$\left\{\begin{array}{cc}\begin{array}{c}{\mathrm{\α}}_{\mathrm{it}}{p}_{i\min }\≤p_{\mathrm{it}}+q_{\mathrm{it}}\left(s^{\′}\right)\≤{\mathrm{\α}}_{\mathrm{it}}{p}_{i\max }\\ {\mathrm{\α}}_{\mathrm{it}}{p}_{i\min }\≤p_{\mathrm{it}}+q_{\mathrm{it}}\left(s^{\′\′}\right)\≤{\mathrm{\α}}_{\mathrm{it}}{p}_{i\max }\end{array}& \∀t,\∀i\end{array}\right.---\left(11\right)$

In formula: p _imax, p _iminbe respectively unit i technology to exert oneself upper and lower limit.

4) on robust track, unit output range constraint:

$\max (p_{i\min },0.9p_{\mathrm{it}}^0){\mathrm{\α}}_{\mathrm{it}}\≤p_{\mathrm{it}}\≤\min (p_{i\max },1.1p_{\mathrm{it}}^0){\mathrm{\α}}_{\mathrm{it}}---\left(12\right)$

This constraint considers that correction is planned a few days ago should be not excessive with original departure degree of planning a few days ago.

Step 5, set up the objective function a few days ago dispatched based on the robust optimization of energy-saving power generation dispatching system of the access such as the multiple-energy-source that is applicable to wind/light/water/gas, minimum for objective function with the generating expense summation of the fuel cost of one day 96 period fired power generating unit and various regenerative resource.

That is:

$\begin{array}{cc}\min \left\{\underset{s\∈X_j}{\max }\underset{\underset{\mathrm{\Ω}(a,X_j)}{s}}{\min }\right\{\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{f}_i[p_{\mathrm{it}}+q_{\mathrm{it}}\left(s\right)]{\mathrm{\α}}_{\mathrm{it}}+\mathrm{\Σ\Σ}{C}_{\mathrm{\λ}}{P}_{\mathrm{\λ}}\left\}\right\}& \∀t\end{array}---\left(13\right)$

Wherein: s is certain scene specific; X _jthe column vector of corresponding renewable energy power generation power residing scene under 96 periods; T is period sequence number, t=1,2, K, T; α _itfor the running status of unit i t period, value 0 or 1; p _itfor the plan on fired power generating unit i t period robust track is exerted oneself; q _its () is exerted oneself for the adjustment of fired power generating unit i t period under scene s; N _gfor fired power generating unit number of units; C _λfor the cost that other new forms of energy specific powers such as photovoltaic, combustion gas export; P _λfor other new forms of energy such as photovoltaic, combustion gas export general power.

Wherein:

$f_i\left(p_{\mathrm{it}}\right)=a_i{p}_{\mathrm{it}}^2+b_i{p}_{\mathrm{it}}+c_i---\left(14\right)$

Be respectively the energy consumption quadratic term of unit i, Monomial coefficient and constant term coefficient.

Step 6, adopt particle swarm optimization algorithm robust Optimal Operation Model, optimizing obtains globally optimal solution, revises generation schedule a few days ago.

The iterations arranging artificial intelligence optimization's algorithm in example is 100.Calculated by program, show that robust optimizes operation plan and total cost of electricity-generating a few days ago.Carry out 10 times to same group of data source to calculate, as shown in Figure 6, definition:

(15)

From accompanying drawing 6, each cost of electricity-generating maximum deviation calculating gained total is less than 0.001, good convergence.Accompanying drawing 7 is that in example, certain Power Plant #1 robust optimizes scheduling result a few days ago.

Setting small hydropower system installation total volume be 2000MW, when the installation total volume of small hydropower system, photovoltaic plant, wind energy turbine set, fuel engine power generation is 4000MW, optimize dispatching algorithm a few days ago by robust, calculate the positive and negative as shown in Figure 8 for subsequent use of the fired power generating unit in 96 periods.

The above; be only the specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, anyly belongs to 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 (2)

1. the robust being applicable to various energy resources access optimizes dispatching method a few days ago, it is characterized in that, the objective function a few days ago dispatched based on the robust optimization of energy-saving power generation dispatching system and the robust Optimized Operation constraint condition of the access of the various energy resources such as wind/light/water/gas is applicable to based on one, this objective function is minimum for target with the generating expense summation of the fuel cost of one day 96 period fired power generating unit and various regenerative resource, and formula is as follows:

That is:

$\min \left\{\underset{s\∈X_j}{\max }\underset{\underset{\mathrm{\Ω}(\mathrm{\α},X_j)}{s}}{\min }\right\{\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{f}_i[p_{\mathrm{it}}+q_{\mathrm{it}}\left(s\right)]{\mathrm{\α}}_{\mathrm{it}}+\mathrm{\Σ\Σ}{C}_{\mathrm{\λ}}{P}_{\mathrm{\λ}}\left\}\right\}$ Formula one

Wherein: s is certain scene specific; X _jthe column vector of corresponding renewable energy power generation power residing scene under 96 periods; T is period sequence number, t=1,2, K, T; α _itfor the running status of unit i t period, value 0 or 1,0 represents shuts down, 1 representative start; p _itfor the plan on fired power generating unit i t period robust track is exerted oneself; q _its () is exerted oneself for the adjustment of fired power generating unit i t period under scene s; N _gfor fired power generating unit number of units; C _λfor the cost that other new forms of energy specific powers such as photovoltaic, combustion gas export; P _λfor other new forms of energy such as photovoltaic, combustion gas export general power;

Wherein:

$f_i\left(p_{\mathrm{it}}\right)=a_i{p}_{\mathrm{it}}^2+b_i{p}_{\mathrm{it}}+c_i$ Formula two

A _i, b _i, c _ibe respectively the energy consumption quadratic term of unit i, Monomial coefficient and constant term coefficient;

Described robust Optimized Operation constraint condition set up the demand of system reserve based on multiple energy/source accesses such as quantification wind/light/aqueous vapors, is divided into equality constraint and inequality constrain condition; Respectively:

Equality constraint one: active power balance under consideration new forms of energy predicated error, formula is as follows:

$\left\{\begin{array}{c}\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}^0=\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}[p_{\mathrm{it}}+q_{\mathrm{it}}\left(s^{\′}\right)]+{\mathrm{\ΔP}}_{\mathrm{it}}^{\′}\\ \underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}^0=\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}[p_{\mathrm{it}}+q_{\mathrm{it}}\left(s^{\′\′}\right)]+\mathrm{\Δ}{P}_{\mathrm{it}}^{\′\′}\\ \underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}^0=\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{p}_{\mathrm{it}}+\mathrm{\Δ}\stackrel{\‾}{P_t}\end{array}\right.,\∀t$ Formula three

In formula: exert oneself for adjusting the plan of fired power generating unit in the t period that energy-saving power generation dispatching system provides; p _itexert oneself for the unit i t period is meritorious; q _it(s') under scene s', the adjustment of fired power generating unit i t period is adjusted to exert oneself, q _it(s ") is scene s " descend the adjustment of middle tune fired power generating unit i t period to exert oneself; S', s " residing for current scene s up-and-down boundary scene (s "≤s≤s'); Δ P ' _itfor the renewable energy power generation power prediction error of t period under scene s', " the renewable energy power generation power prediction error of lower t period for scene s; it is the renewable energy power generation power prediction AME of t period;

Inequality constrain condition one: middle tune fired power generating unit day part adjusts force constraint, and formula is as follows:

$\left\{\begin{array}{c}-\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{dn}}\≤q_{\mathrm{it}}\left(s^{\′}\right)\≤\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{up}}\\ -\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{dn}}\≤q_{\mathrm{it}}\left(s^{\′\′}\right)\≤\mathrm{\Δt}\×{\mathrm{\Δp}}_{i,\mathrm{up}}\end{array}\right.,\∀t,\∀i$ Formula four

In formula: Δ p _{i, up}, Δ p _{i, dn}be respectively unit i exert oneself rise, lower maximum rate; Δ t is the middle tune fired power generating unit spinning reserve response time, is set to 5min;

Inequality constrain condition two: middle tune fired power generating unit exert oneself bound constraint, formula is as follows:

$\left\{\begin{array}{c}{\mathrm{\α}}_{\mathrm{it}}{p}_{i\min }\≤p_{\mathrm{it}}+q_{\mathrm{it}}\left(s^{\′}\right)\≤{\mathrm{\α}}_{\mathrm{it}}{p}_{i\max }\\ {\mathrm{\α}}_{\mathrm{it}}{p}_{i\min }\≤p_{\mathrm{it}}+q_{\mathrm{it}}\left(s^{\′\′}\right)\≤{\mathrm{\α}}_{\mathrm{it}}{p}_{i\max }\end{array}\right.,\∀t,\∀i$ Formula five

In formula: p _imax, p _iminbe respectively unit i technology to exert oneself upper and lower limit;

Inequality constrain condition three: on robust track, unit output range constraint, formula is as follows:

$\max (p_{i\min },0.9p_{\mathrm{it}}^0){\mathrm{\α}}_{\mathrm{it}}\≤p_{\mathrm{it}}\≤\min (p_{i\max },1.1p_{\mathrm{it}}^0){\mathrm{\α}}_{\mathrm{it}}$ Formula six

This constraint considers that correction is planned a few days ago should be not excessive with original departure degree of planning a few days ago;

This robust being applicable to various energy resources access is optimized dispatching method a few days ago and is comprised the following steps:

Step 1, obtains the middle tune fired power generating unit of energy-saving power generation dispatching system formulates and generation schedule a few days ago, the unit parameter of Hydropower Unit; According to energy-conservation, economic principle, priority scheduling Hydropower Unit, does not change the generation schedule a few days ago of Hydropower Unit;

Step 2, obtains the short-term forecasting value of wind-powered electricity generation and small power station, photovoltaic, fuel gas generation etc., by various renewable energy power generation power by the some scenes of exerting oneself of the setting ratio setting of respective installed capacity;

Step 3, adopt particle swarm optimization algorithm robust Optimal Operation Model, optimizing obtains globally optimal solution, and the plan namely obtained under each unit robust track is exerted oneself p _itto exert oneself q with adjustment _it(s'), q _it(s "); In searching process, initially exerting oneself of unit is arranged at random in the interval that formula six is determined, iteration convergence condition be the knots modification of global optimum's particle adaptive value continuous K time within the scope of convergence precision, iteration convergence precision setting is 0.001, wherein, K gets the positive integer being more than or equal to 20.

2. a kind of robust being applicable to various energy resources access according to claim 1 optimizes dispatching method a few days ago, it is characterized in that, in described step 2, obtain the short-term forecasting value of wind-powered electricity generation and small power station, photovoltaic, fuel gas generation etc., various renewable energy power generation power is arranged some scenes of exerting oneself by the certain proportion of respective installed capacity;

According to the renewable energy power generation power short-term forecasting value of 96 periods, respectively the interval residing for various renewable energy power generation power is judged to each period, and then the scene under determining each period residing for various renewable energy power generation power; Finally, the matrix of the various renewable energy power generation power of reflection corresponding different scene under Different periods can be formed:

[X ₁, X ₂..., X _j, X _n] formula seven

Wherein, X ₁the column vector of corresponding wind-powered electricity generation residing scene under 96 periods, X _jrepresent that other regenerative resources are as small power station, photovoltaic, combustion gas.