1. A stochastic dynamical unit commitment method for a power system based on solving quantiles via Newton method, comprising the following steps:(1) establishing a stochastic dynamical unit commitment model with chance constraints based on solving quantiles of random variables via Newton method, the stochastic dynamical unit commitment model comprising an objective function and constraints, the establishing comprising:

(1-1) establishing the objective function of the stochastic dynamical unit commitment model with chance constraints based on solving quantiles of random variables via Newton method,

the objective function for minimizing a sum of power generating costs and on-off costs of thermal power generating units by a formula of:

where, T denoting the number of dispatch intervals t; NG denoting the number of thermal power generating units in the power system; t denoting a serial number of dispatch intervals; i denoting a serial number of thermal power generating units; Pit denoting an active power of thermal power generating unit i at dispatch interval t; CFi denoting a fuel cost function of thermal power generating unit i; CUit denoting a startup cost of thermal power generating unit i at dispatch interval t; and CDit denoting a shutdown cost of thermal power generating unit i at dispatch interval t;

the fuel cost function of the thermal power generating unit being expressed as a quadratic function of the active power of the thermal power generating unit by a formula of:

CFi(Pit)=ai(Pit)2+biPit+ci,

where, ai denoting a quadratic coefficient of a fuel cost of thermal power generating unit i; bi denoting a linear coefficient of the fuel cost of thermal power generating unit i; ci denoting a constant coefficient of the fuel cost of thermal power generating unit i; and values of ai, bi, and ci being obtained from a dispatch center;

the startup cost of the thermal power generating unit, and the shutdown cost of the thermal power generating unit being denoted by formulas of:

CUit?Ui(vit?vit?1)

CUit?0

CDit?Di(vit?1?vit)

CDit?0,

where, vit denoting a state of thermal power generating unit i at dispatch interval t; in which, if vit=0, it represents that thermal power generating unit i is in an off state; if vit=1, it represents that thermal power generating unit i is in an on state; it is set that there is the startup cost when the unit is switched from the off state to the on state, and there is the shutdown cost when the unit is switched from the on state to the off state; Ui denoting a startup cost when thermal power generating unit i is turned on one time; and Di denoting a shutdown cost when thermal power generating unit i is turned off one time;

(1-2) obtaining constraints of the stochastic dynamical unit commitment model with chance constraints based on solving quantiles of random variables via Newton method, comprising:

(1-2-1) obtaining a power balance constraint of the power system by a formula of:

where, Pit denoting a scheduled active power of thermal power generating unit t at dispatch interval t; wtj denoting a scheduled active power of renewable energy power station at dispatch interval t; dmt denoting a size of load m at dispatch interval t; and ND denoting the number of loads in the power system;

(1-2-2) obtaining an upper and lower constraint of the active power of the thermal power generating unit in the power system by a formula of:

Pivit?Pit?Pivit,

where, Pi denoting an active power lower limit of thermal power generating unit i; Pi denoting an active power upper limit of thermal power generating unit i; vit denoting the state of thermal power generating unit a at dispatch interval t; in which if vit=0, it represents that thermal power generating unit is in an on state; and if vit=1, it represents that thermal power generating unit i is in an off state;

(1-2-3) obtaining a reserve constraint of the thermal power generating unit in the power system, by a formula of:

Pit+rit+?Pivit

0?rit+?ri+

Pit?rit??Pivit

0?rit??ri?,

where, rit+ denoting an upper reserve of thermal power generating unit i at dispatch interval t; rit? denoting a lower reserve of thermal power generating unit i at dispatch interval t; ri+ denoting a maximum upper reserve of thermal power generating unit i at dispatch interval t; ri? denoting a maximum lower reserve of thermal power generating unit i at dispatch interval t; and the maximum upper reserve and the maximum lower reserve being obtained from the dispatch center of the power system;

(1-2-4) obtaining a ramp constraint of the thermal power generating unit in the power system, by a formula of:

Pit?Pit?1??RDi?T?(2?vit?vit?1)Pi

Pit?Pit?1?RUi?T+(2?vit?vit?1)Pi,

where, RUi denoting upward ramp capacities of thermal power generating unit i, RDi and denoting downward ramp capacities of thermal power generating unit i, which are obtained from the dispatch center; and ?T denoting an interval between two adjacent dispatch intervals;

(1-2-5) obtaining a constraint of a minimum continuous on-off period of the thermal power generating unit in the power system, comprising:

obtaining a minimum interval for power-on and power-off switching of the thermal power generating unit by a formula of:

where, UTi denoting a minimum continuous startup period, and DTi denoting a minimum continuous shutdown period;

(1-2-6) obtaining a reserve constraint of the power system by a formula of:

where, wjt denoting an actual active power of renewable energy power station j at dispatch interval t; wtj denoting a scheduled active power of renewable energy power station at dispatch interval t; R+ and R? denoting additional reserve demand representing the power system from the dispatch center; ?r+ denoting a risk of insufficient upward reserve in the power system; ?r? denoting a risk of insufficient downward reserve in the power system; and Pr(·) denoting a probability of occurrence of insufficient upward reserve and a probability of occurrence of insufficient downward reserve; the probability of occurrence of insufficient upward reserve and the probability of occurrence of insufficient downward reserve being obtained from the dispatch center;

(1-2-7) obtaining a branch flow constraint of the power system by a formula of

where, Gl,i denoting a power transfer distribution factor of branch l to the active power of thermal power generating unit i; Gl,j denoting a power transfer distribution factor of branch l to the active power of renewable energy power station j; Gl,m denoting a power transfer distribution factor of branch l to load m; each power transfer distribution factor being obtained from the dispatch center; Ll denoting an active power upper limit on branch l; and ? denoting a risk level of an active power on the branch of the power system exceeding a rated active power upper limit of the branch, which is determined by a dispatcher;

(2) based on the objective function and constraints of the stochastic dynamical unit commitment model, employing the Newton method to solve quantiles of random variables, comprising:

(2-1) converting the chance constraints into deterministic constraints containing quantiles, comprising:

denoting a general form of the chance constraints by a formula of:

Pr(cTwt+dTx?e)?1?p,

where, c and d denoting constant vectors with NW dimension in the chance constraints; NW denoting the number of renewable energy power stations in the power system; e denoting constants in the chance constraints; P denoting a risk level of the chance constraints, which is obtained from the dispatch center in the power system; wtj denoting an actual active power vector of all renewable energy power stations at dispatch interval t; and x denoting a vector consisting of decision variables, and the decision variables being scheduled active powers of the renewable energy power stations and the thermal power generating units;

converting the general form of the chance constraints to the deterministic constraints containing the quantiles, by a formula of:

denoting quantiles when a probability of one-dimensional random variables cTwt is equal to 1?p;

(2-2) setting a joint probability distribution of the actual active powers of all renewable energy power stations in the power system to satisfy the following Gaussian mixture model:

where, wtj denoting a set of scheduled active powers of all renewable energy power stations in the power system; wtj being a stochastic vector;

denoting a probability density function of the stochastic vector; Y denoting values of wtj; N(Y, ?s, ?s) denoting the s?th component of the mixed Gaussian distribution; n denoting the number of components of the mixed Gaussian distribution; ?s denoting a weighting coefficient representing the s?th component of the mixed Gaussian distribution and a sum of weighting coefficients of all components is equal to 1; ?s denoting an average vector of the s?th component of the mixed Gaussian distribution; ?s is denoting a covariance matrix of the s?th component of the mixed Gaussian distribution; det (?s) denoting a determinant of the covariance matrix ?s; and a superscript T indicating a transposition of matrix;

acquiring a nonlinear equation containing the quantiles

as follows:

where, ?(·) denoting a cumulative distribution function representing a one-dimensional standard Gaussian distribution; y denoting a simple expression representing the quantile;

and ?s denoting an average vector of the s?th component of the mixed Gaussian distribution;

(2-3) employing the Newton method, solving the nonlinear equation of step (2-2) iteratively to obtain the quantiles

of the random variables cTwt, comprising:

(2-3-1) initialization:

setting an initial value of y to y0, by a formula of:

y0=max(cT?i,i?{1,2, . . . ,NW});

(2-3-2) iteration:

updating a value of y by a formula of:

denoting quantiles when a probability of one-dimensional random variables cTwt is equal to 1?p; yk denoting a value of y of a previous iteration; yk+1 denoting a value of y of a current iteration, which is to be solved; and

denoting a probability density function representing the stochastic vector cTwt, which is denoted by a formula of:

(2-3-3) setting an allowable error ? of the iterative calculation; judging an iterative calculation result based on the allowable error, in which, if

it is determined that the iterative calculation converges, and values of the quantilesof the random variables are obtained; and ifit is returned to (2-2-2);(3) obtaining an equivalent form

of the chance constraints in the step (1-2-6) and the step (1-2-7) based onin the step (2); using a branch and bound method, solving the stochastic dynamical unit commitment model comprising the objective function and the constraints in the step (1) to obtain vit, Pit, and wjt, in which, vit is taken as a starting and stopping state of thermal power generating unit i at dispatch interval t; Pit is taken as a scheduled active power of renewable energy power station j at dispatch interval t; wjt and is taken as a reference active power of renewable energy power station j at dispatch interval t, solving the stochastic dynamical unit commitment model with chance constraints based on solving quantiles via Newton method; andcontrolling the thermal power generating unit and the renewable energy power station in the power system based on the vit, Pit, and wjt.