CN105262145A - An optimal selection method for new energy mixed system control parameters - Google Patents

Abstract

The invention discloses an optimal selection method for new energy mixed system control parameters, and is used for carrying out optimal selection on PID control parameters in a new energy mixed system. A model is established according to the new energy mixed system; and then an objective function with PID controller input and output parameters as state variables is established according to a simulation system; and optimal PID control parameters are obtained by means of solving the objective function through utilization of the optimal selection method designed in the invention. The optimal selection method for new energy mixed system control parameters of the invention employs a novel heuristic optimization algorithm to optimize the objective function, and better objective function values can be searched, so that obtained solutions represent better PID control parameters. The better PID control parameters enable the frequency offset of the new energy mixed system to be smaller, the adjusting speed to be faster, the system response curve to be more smooth and the system adjusting quality to be higher.

Description

A kind of method for optimizing of new forms of energy hybrid system controling parameters

Technical field

The invention belongs to technical field of new energies, more specifically, relate to a kind of method for optimizing of new forms of energy hybrid system controling parameters, carry out preferably for comparative example integral differential (ProportionIntegrationDifferentiation, PID) controling parameters in new forms of energy hybrid system.

Background technology

Along with the lifting of energy demand and the impact of global warming considerations, the exploitation of the new forms of energy such as wind energy, the sun and grid-connectedly become active demand.It is very large that wind energy and the sun go out fluctuation, for stabilizing the fluctuation of exerting oneself of new forms of energy, generally increasing energy storage device and opening and closing generating set flexibly, form new forms of energy hybrid system with it, exerted oneself by adjustment System, make it responding system load, thus make system frequency fast and stable.

New forms of energy hybrid system generally adopts PID to control, and comprises Traditional PID, Fractional Order PID and fuzzy etc.The selection of control parameter of PID controller directly affects the Control platform of new forms of energy hybrid system.Traditional pid control parameter method for optimizing comprises grid search (GridSearch, GS), particle swarm optimization algorithm (ParticleSwarmoptimization, PSO) etc.Because PSO algorithm exists precocity, is absorbed in the deficiencies such as local minimum in complicated optimum problem solves, optimum new forms of energy hybrid system controling parameters possibly cannot be obtained.

Summary of the invention

For the deficiency of conventional method, the present invention proposes a kind of method for optimizing of new forms of energy hybrid system controling parameters, the method, based on novel heuristic value, effectively can improve new forms of energy hybrid system Control platform, improves this energy mix stability of a system.

To achieve these goals, the invention provides a kind of method for optimizing of new forms of energy hybrid system controling parameters, comprise the steps:

Step (1): the simulation model setting up new forms of energy hybrid system, new forms of energy hybrid system as shown in Figure 1.Described new forms of energy hybrid system comprises wind power generation system, solar heat power generation system, water electrolyser, fuel cell, flywheel energy storage system, battery energy storage system, super capacitor, diesel engine generator and micro electric Force system.Described new forms of energy hybrid system describes considers that wind energy and solar energy Stochastic sum are intermittent, and under the factor such as the fluctuation of load, the energy management of micro electric Force system and control principle.The part that wind power generation system and solar heat power generation system entirety are exerted oneself produces hydrogen by driving water electrolyser, the hydrogen of generation carries out injecting micro electric Force system after generating produces metastable exerting oneself by recycling fuel cell, and remaining wind energy and solar energy are exerted oneself and directly injected micro electric Force system; Because wind energy and solar energy have randomness, simultaneity factor load also has fluctuation, after there is deviation in system injecting power and load, micro electric Force system will produce frequency offset, PID controller produces regulable control signal according to frequency offset, control flywheel energy storage system, battery energy storage system, super capacitor and diesel engine generator work, eliminate miniature Power Systems and load deviation rapidly.Conventional controller comprises, PID controller and the modified model such as Fractional Order PID Controller, fuzzy controller PID controller.For convenience of description, the present invention is using PID controller as new forms of energy hybrid system controller.Adopt the new forms of energy hybrid system transfer function figure of PID controller as shown in Figure 1.K in PID controller _p, K _iand K _dbeing respectively ratio, integration and differentiation gain, is the controling parameters needing to adjust;

Step (2): the Optimization about control parameter target function setting up above-mentioned new forms of energy hybrid system.The controlled quentity controlled variable u exported with frequency deviation f and controller is for quantity of state, and target function is:

$\min f\left(X\right)={\∫}_0^{T_{\max }}\left(w_1{\left(\mathrm{\Δ}f\right)}^2+w_2{(u-u_{ss})}^2\right)dt$

Wherein, optimized variable X=[K _p, K _i, K _d], Δ f is the new forms of energy hybrid system frequency departure with controling parameters change, and u is that PID controller exports; w ₁and w ₂weight coefficient, T _maxfor emulation total time, u _ssfor steady state control signal, expression formula is u _ss=0.8H (t)+0.17H (t-40)+1.12H (t-80), wherein H (t) is He Wei Saden step function.Expression is:

$H\left(t\right)=\left\{\begin{array}{c}0,t<0\\ 0.5,t=0\\ 1,t>0\end{array}\right.$

Step (3): use target function in heuristic value solution procedure (2), obtains optimal control parameter.

Step1: algorithm initialization: arrange algorithm parameter, comprises population size N, total number of iterations T, individual random search sum N _l, eliminate range coefficient σ, skip threshold p; Determine pid control parameter scope, K _p∈ [K _{p, min}, K _{p, max}], K _i∈ [K _{i, min}, K _{i, max}], K _d∈ [K _{d, min}, K _{d, max}], determine optimized variable border [B _l, B _u], B _l=[K _{p, min}, K _{i, min}, K _{d, min}], B _u=[K _{p, max}, K _{i, max}, K _{d, max}], K _{p, min}, K _{p, max}be respectively minimum value and the maximum of proportional control factor, K _{i, min}, K _{i, max}be respectively minimum value and the maximum of integral control coefficient, K _{d, min}, K _{d, max}be respectively minimum value and the maximum of derivative control coefficient, the position vector of all individualities in this interval random initializtion colony, individual position vector X _i=[K _p, K _i, K _d], i=1 ..., N, represents one group of controling parameters; Make current iteration number of times t=0;

Step2: the target function value F calculating each individuality _i ^t=f (X _i(t)), i=1 ..., N.Process is as follows: from individual i position vector X _it () decoding obtains controling parameters, wherein K _p, K _iand K _dbe respectively first, second, and third element in position vector, controling parameters substituted into new forms of energy hybrid system simulation platform in step (1), emulation obtains system state variables process over time.Obtain system frequency deviation Δ f and controller output u, obtain the target function value F of individual i according to target function in step (2) _i ^t.Further, calculate target population function minimum, the individuality with minimum target functional value is defined as current optimum individual X _b(t);

Step3: to all individual X _i, i=1 ..., N carries out individual random search, calculates inertia vector

Step3.1: make individual searching times l=0;

Step3.2: look around a position calculate i=1 ..., N:

$X_i^{play}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;{\mathrm{\&epsiv;}}_{play}$

Rand is random number between (0,1), ε _playfor looking around step-length, ε _play=0.1||B _u-B _l||;

Step3.3: calculate next current location

$\left\{\begin{array}{cc}{X}_i^{self}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;\frac{X_i^{play}\left(t\right)-X_i\left(t\right)}{\left|\right|X_i^{play}\left(t\right)-X_i\left(t\right)\left|\right|}\&CenterDot;{\mathrm{\&epsiv;}}_{step}& iff\left(X_i^{play}\left(t\right)\right)<f\left(X_i\left(t\right)\right)\\ X_i^{self}\left(t\right)=X_i\left(t\right)& iff\left(X_i^{play}\left(t\right)\right)\&GreaterEqual;f\left(X_i\left(t\right)\right)\end{array}\right.$

Rand is random number between (0,1), ε _stepfor inertia step-length, ε _step=0.2||B _u-B _l||;

Step3.4:l=l+1, if l < is N _l, go to Step3.2; Otherwise, go to Step4;

Step4: calculate each individuality by current optimum individual calling vector

$\left\{\begin{array}{c}{X}_i^{bw}\left(t\right)=X_B\left(t\right)+c_2\&CenterDot;{\mathrm{\&delta;}}_i\\ {\mathrm{\&delta;}}_i=|c_1\&CenterDot;X_B\left(t\right)-X_i\left(t\right)|\end{array}\right.$

Wherein δ _ifor in the distance vector of i-th individuality and current optimum individual, random number c ₁=2rand, c ₂=(2rand-1) (1-t/T) is random number between (0,1); It can thus be appreciated that c ₁for the random number between (0,2), represent the appeal of current optimum individual, work as c ₁during > 1, represent that the influence power of current optimum individual strengthens, otherwise weaken; c ₂for dynamic random number, so c ₂random scope by 1 also linear decrease to 0;

Step5: upgrade a body position according to individual location updating formula:

$X_i(t+1)=2\&CenterDot;rand\&CenterDot;X_i^{bw}\left(t\right)+rand\&CenterDot;X_i^{self}\left(t\right)$

Step6: judge individual the need of being eliminated and reinitializing:

Step6.1: if i-th individuality meets formula, this individuality is eliminated and reinitializes:

$F_i^t>F_{ave}^t+\mathrm{\&omega;}\&CenterDot;(F_{ave}^t-F_{\min }^t),i=1,...,N$

Wherein, the mean value of t for all individual goal functional values of population, be minimum target function value, ω is the parameter of a linear increment with iterations, span is [-σ, σ];

Step6.2: the individual initialization be eliminated:

X _i＝rand(1,D)×(B _U-B _L)+B _L

Wherein, D is position vector dimension, D=3;

Step7: judge whether that continuous p is not moved for current optimum individual position, if so, then think population extinction, inverting reconstructs new population according to the following formula:

$X_i=X_B+rand\&times;\frac{R^2}{{\mathrm{\&delta;}}_i},i=1,2,...,N$

Wherein R is radius of inversion, R=0.1||B _u-B _l||; Rand is random number between (0,1), and p is skip threshold;

Step8:t=t+1, if t>T, algorithm terminates, and exports current optimum individual position as whole solution; Otherwise, proceed to Step2.Current optimum individual position is optimal control parameter vector.

Compared with prior art, the present invention has the following advantages and effect:

The method for optimizing of the new forms of energy hybrid system controling parameters of the present invention's design, adopts a kind of novel heuristic value optimization object function, can search more excellent target function value, the pid control parameter that the solution representative obtained is more excellent.More excellent pid control parameter can make new forms of energy hybrid system frequency departure less, and governing speed is faster, and system responses curve is more smooth, and system fading margin quality is higher.

Accompanying drawing explanation

Fig. 1 is the schematic diagram of new forms of energy hybrid control system of the present invention;

Fig. 2 is the load variations figure of new forms of energy hybrid control system of the present invention;

Fig. 3 is the wind power generation variation diagram of new forms of energy hybrid control system of the present invention;

Fig. 4 is the solar energy thermal-power-generating variation diagram of new forms of energy hybrid control system of the present invention;

Fig. 5 is the controlled quentity controlled variable that new forms of energy hybrid control system controller of the present invention exports;

Fig. 6 is the frequency offset comparison diagram of new forms of energy hybrid control system under the preferred method of the present invention and PSO optimized algorithm.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.In addition, if below in described each execution mode of the present invention involved technical characteristic do not form conflict each other and just can mutually combine.

The present invention relates to a kind of Optimization about control parameter of new forms of energy hybrid system, this system is made up of wind power generation system, solar heat power generation system, water electrolyser, fuel cell, flywheel energy storage system, battery energy storage system, super capacitor, diesel engine generator and micro electric Force system, as shown in Figure 1.The object of the invention is the controling parameters method for optimizing proposing this system a kind of, thus improve new forms of energy hybrid system Control platform, improve this energy mix stability of a system.

For effect of the present invention is described, the inventive method is described in detail using a certain new forms of energy hybrid system as objective for implementation of the present invention below:

Step (1): new forms of energy hybrid system as shown in Figure 1, comprises wind-driven generator system, solar heat power generation system, water electrolyser, fuel cell, flywheel energy storage system, battery energy storage system, super capacitor, diesel engine generator and micro electric Force system.

Described new forms of energy hybrid system describes considers that wind energy and solar energy Stochastic sum are intermittent, and under the factor such as the fluctuation of load, the energy management of micro electric Force system and control principle.The part that wind power generation system and solar heat power generation system entirety are exerted oneself produces hydrogen by driving water electrolyser, the hydrogen of generation carries out injecting micro electric Force system after generating produces metastable exerting oneself by recycling fuel cell, and remaining wind energy and solar energy are exerted oneself and directly injected micro electric Force system; Because wind energy and solar energy have randomness, simultaneity factor load also has fluctuation, after there is deviation in system injecting power and load, micro electric Force system will produce frequency offset, PID controller produces regulable control signal according to frequency offset, control flywheel energy storage system, battery energy storage system, super capacitor and diesel engine generator work, eliminate miniature Power Systems and load deviation rapidly.This system carries out regulable control by PID controller.K _p, K _iand K _dbe respectively ratio, integration and differentiation gain, K _p, K _iand K _dneed preferred pid control parameter.

Set up the simulation model of new forms of energy hybrid system, arrange the various parameters of new forms of energy hybrid system, the parameter of various new forms of energy is as shown in table 1; The iteration step length of new forms of energy hybrid system is 0.05 second, and total simulation time is 120 seconds, and the value arranging each energy initial time is 0, and load initial time value is 1; The load fluctuation of new forms of energy hybrid system as shown in Figure 2; Consider the unsteadiness of wind energy and solar energy in new forms of energy hybrid system, the fluctuation of wind energy and solar energy as shown in Figure 3, Figure 4;

The design parameter of the assembly of table 1 new forms of energy hybrid system

Step (2) sets up the Optimization about control parameter target function of system: the controlled quentity controlled variable u exported with frequency error Δ f and controller is for input variable, and target function is:

$\min f\left(X\right)={\&Integral;}_0^{T_{\max }}\left(w_1{\left(\mathrm{\&Delta;}f\right)}^2+w_2{(u-u_{ss})}^2\right)dt$

Wherein, optimized variable X=[K _p, K _i, K _d], Δ f is the new forms of energy hybrid system frequency departure with controling parameters change, and u is that PID controller exports, as shown in fig. 1, and weight w ₁and w ₂all be set to 1, T _maxfor 120 seconds total times, u _ssfor steady state control signal, expression formula is: u _ss=0.8H (t)+0.17H (t-40)+1.12H (t-80), wherein H (t) is He Wei Saden step function.

Expression is:

$H\left(t\right)=\left\{\begin{array}{c}0,t<0\\ 0.5,t=0\\ 1,t>0\end{array}\right.$

Step (3): use heuristic value to calculate the optimization object function of new forms of energy hybrid system, optimize optimum PID control parameter.

Step1: novel heuristic value parameter is set: total number of iterations T=50, population size N=20, other optimum configurations of algorithm is as follows: individual random search number N _l=3, eliminate range coefficient σ=0.01, skip threshold p=300; Determine pid control parameter scope, K _p∈ [K _{p, min}, K _{p, max}], K _i∈ [K _{i, min}, K _{i, max}], K _d∈ [K _{d, min}, K _{d, max}], determine optimized variable border [B _l, B _u], B _l=[K _{p, min}, K _{i, min}, K _{d, min}], B _u=[K _{p, max}, K _{i, max}, K _{d, max}]; B _l=[0.001,0.001,0.001], B _u=[3,3,3], K _{p, min}, K _{p, max}be respectively minimum value and the maximum of proportional control factor, K _{i, min}, K _{i, max}be respectively minimum value and the maximum of integral control coefficient, K _{d, min}, K _{d, max}be respectively minimum value and the maximum of derivative control coefficient, the position vector of all individualities in this interval initialization colony, individual position vector X _i=[K _p, K _i, K _d], i=1 ..., N, represents one group of controling parameters; Make current iteration number of times t=0;

Step2: the target function value F calculating each individuality _i ^t=f (X _i(t)), i=1 ..., N.Process is as follows: from individual i position vector X _it () decoding obtains controling parameters, wherein K _p, K _iand K _dbe respectively first, second, and third element in position vector, controling parameters substituted into new forms of energy hybrid system simulation platform in step (1), emulation obtains system state variables process over time.Obtain system frequency deviation Δ f and controller output u, obtain the target function value F of individual i according to target function in step (2) _i ^t.Further, calculate target population function minimum, the individuality with minimum target functional value is defined as current optimum individual X _b(t);

Step3: to all individual X _i, i=1 ..., N carries out individual random search, calculates inertia vector

Step3.1: make individual searching times l=0;

Step3.2: look around a position calculate i=1 ..., N:

$X_i^{play}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;{\mathrm{\&epsiv;}}_{play}$

Rand is random number between (0,1), ε _playfor looking around step-length, ε _play=0.1||B _u-B _l||;

Step3.3: calculate next current location

$\left\{\begin{array}{cc}{X}_i^{self}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;\frac{X_i^{play}\left(t\right)-X_i\left(t\right)}{\left|\right|X_i^{play}\left(t\right)-X_i\left(t\right)\left|\right|}\&CenterDot;{\mathrm{\&epsiv;}}_{step}& iff\left(X_i^{play}\left(t\right)\right)<f\left(X_i\left(t\right)\right)\\ X_i^{self}\left(t\right)=X_i\left(t\right)& iff\left(X_i^{play}\left(t\right)\right)\&GreaterEqual;f\left(X_i\left(t\right)\right)\end{array}\right.$

Rand is random number between (0,1), ε _stepfor inertia step-length, ε _step=0.2||B _u-B _l||;

Step3.4:l=l+1, if l < is N _l, go to Step3.2; Otherwise, go to Step4;

Step4: calculate each individuality by current optimum individual calling vector

$\left\{\begin{array}{c}{X}_i^{bw}\left(t\right)=X_B\left(t\right)+c_2\&CenterDot;{\mathrm{\&delta;}}_i\\ {\mathrm{\&delta;}}_i=|c_1\&CenterDot;X_B\left(t\right)-X_i\left(t\right)|\end{array}\right.$

δ _ifor in the distance vector of i-th individuality and current optimum individual, random number c ₁=2rand, c ₂=(2rand-1) (1-t/T) is random number between (0,1); It can thus be appreciated that c ₁for the random number between (0,2), represent the appeal of current optimum individual, work as c ₁during > 1, represent that the influence power of current optimum individual strengthens, otherwise weaken; c ₂for dynamic random number, so c ₂random scope by 1 also linear decrease to 0;

Step5: upgrade a body position according to individual location updating formula:

$X_i(t+1)=2\&CenterDot;rand\&CenterDot;X_i^{bw}\left(t\right)+rand\&CenterDot;X_i^{self}\left(t\right)$

Step6: judge individual the need of being eliminated and reinitializing:

Step6.1: if i-th individuality meets formula, this individuality is eliminated and reinitializes:

$F_i^t>F_{ave}^t+\mathrm{\&omega;}\&CenterDot;(F_{ave}^t-F_{\min }^t),i=1,...,N$

Wherein, the mean value of t for all individual goal functional values of population, be minimum target function value, ω is the parameter of a linear increment with iterations, span is [-σ, σ];

Step6.2: the individual initialization be eliminated:

X _i＝rand(1,D)×(B _U-B _L)+B _L

Wherein, D is position vector dimension, D=3;

Step7: judge whether that continuous p is not moved for current optimum individual position, if so, then think population extinction, inverting reconstructs new population according to the following formula:

$X_i=X_B+rand\&times;\frac{R^2}{{\mathrm{\&delta;}}_i},i=1,2,...,N$

Wherein R is radius of inversion, R=0.1||B _u-B _l||; Rand is random number between (0,1);

Step8:t=t+1, if t>T, algorithm terminates, and exports current optimum individual position as whole solution; Otherwise, proceed to Step2.Current optimum individual position is optimal control parameter vector.

It is K that Optimization Solution obtains most Zhongdao pid control parameter _p=1.823, K _i=0.797, K _d=0.945.After parameter optimization, the PID controller output signal u of new forms of energy hybrid system as shown in Figure 5.

For comparing the performance of the method for the invention, contrast with the new forms of energy hybrid system controling parameters method for optimizing of tradition based on PSO algorithm.The setting parameter of PSO algorithm is: Population Size N=20, iterations T=50, maximum inertial factor W _max=0.9, minimum inertial factor W _min=0.1, inertia weight W=0.5, self learning rate F _i=2, social learning leads F _g=2, the pid parameter obtained is: K _p=2.04, K _i=0.64, K _d=0.61.The system responses that pid control parameter the present invention preferably obtained is corresponding in new forms of energy hybrid system simulation model with the pid control parameter that PSO algorithm obtains contrasts, and the frequency departure comparing result of system as shown in Figure 6.Comparing result shows, when the pid control parameter adopting the method for the invention to obtain controls new forms of energy hybrid system, the system fading margin time is faster, and the number of oscillation is less, and whole curve is also more smooth.

Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention; not in order to limit the present invention, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.

Claims (7)

1. a method for optimizing for new forms of energy hybrid system controling parameters, is characterized in that, described method comprises the steps:

Step (1): the simulation model setting up new forms of energy hybrid system, described new forms of energy hybrid system comprises wind power generation system, solar heat power generation system, water electrolyser, fuel cell, flywheel energy storage system, battery energy storage system, super capacitor, diesel engine generator and micro electric Force system, the part that wherein wind power generation system and solar heat power generation system entirety are exerted oneself produces hydrogen by driving water electrolyser, the hydrogen of generation carries out injecting micro electric Force system after generating produces metastable exerting oneself by recycling fuel cell, remaining wind energy and solar energy are exerted oneself and are directly injected micro electric Force system, after there is deviation in system injecting power and load, micro electric Force system will produce frequency offset, PID controller produces regulable control signal according to frequency offset, control flywheel energy storage system, battery energy storage system, super capacitor and diesel engine generator work, eliminate miniature Power Systems and load deviation,

Step (2): the Optimization about control parameter target function setting up above-mentioned new forms of energy hybrid system, the controlled quentity controlled variable u exported with frequency deviation f and controller is for quantity of state, and target function is:

$\min f\left(X\right)={\&Integral;}_0^{T_{max}}(w_1{\left(\mathrm{\&Delta;}f\right)}^2+w_2{\left(u-u_{ss}\right)}^2)dt$

Wherein, optimized variable X=[K _p, K _i, K _d], K _p, K _iand K _dbe respectively ratio, integration and differentiation gain, Δ f is the new forms of energy hybrid system frequency departure with controling parameters change, and u is that PID controller exports; w ₁and w ₂weight coefficient, T _maxfor emulation total time, u _ssfor steady state control signal;

Step (3): use the target function in heuristic value solution procedure (2), obtains optimal control parameter.

2. the method for claim 1, is characterized in that, described step (3) specifically comprises following sub-step:

Step1: algorithm initialization: arrange algorithm parameter, comprises population size N, total number of iterations T, individual random search sum N _l, eliminate range coefficient σ, skip threshold p; Determine pid control parameter scope, K _p∈ [K _{p, min}, K _{p, max}], K _i∈ [K _{i, min}, K _{i, max}], K _d∈ [K _{d, min}, K _{d, max}], determine optimized variable border [B _l, B _u], B _l=[K _{p, min}, K _{i, min}, K _{d, min}], B _u=[K _{p, max}, K _{i, max}, K _{d, max}], K _{p, min}, K _{p, max}be respectively minimum value and the maximum of proportional control factor, K _{i, min}, K _{i, max}be respectively minimum value and the maximum of integral control coefficient, K _{d, min}, K _{d, max}be respectively minimum value and the maximum of derivative control coefficient, the position vector of all individualities in this interval random initializtion colony, individual position vector X _i=[K _p, K _i, K _d], i=1 ..., N, represents one group of controling parameters; Make current iteration number of times t=0;

Step2: the target function value F calculating each individuality _i ^t=f (X _i(t)), i=1 ..., N, and find target population function minimum, the individuality with minimum target functional value is defined as current optimum individual X _b(t);

Step3: to all individual X _i, i=1 ..., N, carries out individual random search, calculates inertia vector

Step4: calculate each individuality current optimum individual calling vector

$\left\{\begin{array}{c}{X}_i^{bw}\left(t\right)=X_B\left(t\right)+c_2\&CenterDot;{\mathrm{\&delta;}}_i\\ {\mathrm{\&delta;}}_i=|c_1\&CenterDot;X_B\left(t\right)-X_i\left(t\right)|\end{array}\right.$

Wherein δ _ifor in the distance vector of i-th individuality and current optimum individual, random number c ₁=2rand, c ₂=(2rand-1) (1-t/T) is random number between (0,1);

Step5: upgrade a body position according to individual location updating formula:

$X_i(t+1)=2\&CenterDot;rand\&CenterDot;X_i^{bw}\left(t\right)+rand\&CenterDot;X_i^{self}\left(t\right)$

Step6: judge individual the need of being eliminated and reinitializing:

Step6.1: if i-th individuality meets formula, this individuality is eliminated and reinitializes:

$F_i^t>F_{ave}^t+\mathrm{\&omega;}\&CenterDot;(F_{ave}^t-F_{\min }^t),i=1,...,N$

Wherein, the mean value of t for all individual goal functional values of population, be minimum target function value, ω is the parameter of a linear increment with iterations, span is [-σ, σ];

Step6.2: the individual initialization be eliminated:

X _i＝rand(1,D)×(B _U-B _L)+B _L

Wherein, D is position vector dimension, D=3;

Step7: judge whether that continuous p is not moved for current optimum individual position, if so, then think population extinction, inverting reconstructs new population according to the following formula:

$X_i=X_B+rand\&times;\frac{R^2}{{\mathrm{\&delta;}}_i},i=1,2,...,N$

Wherein R is radius of inversion, R=0.1||B _u-B _l||; Rand is random number between (0,1), and p is skip threshold;

Step8:t=t+1, if t>T, algorithm terminates, and export current optimum individual position as whole solution, current optimum individual position is optimal control parameter vector; Otherwise, proceed to Step2.

3. method as claimed in claim 2, is characterized in that, calculate the target function value F of each individuality in described step Step2 _i ^t=f (X _i(t)) be specially:

From individual i position vector X _it () decoding obtains controling parameters, wherein K _p, K _iand K _dbe respectively first, second, and third element in position vector, controling parameters is substituted into new forms of energy hybrid system simulation platform in step (1), emulation obtains system state variables process over time, obtain system frequency deviation Δ f and controller output u, obtain the target function value F of individual i according to target function in step (2) _i ^t.

4. method as claimed in claim 2 or claim 3, it is characterized in that, described step Step3 specifically comprises following sub-step:

Step3.1: make individual searching times l=0;

Step3.2: look around a position calculate $X_i^{play}\left(t\right),i=1,...,N:$

$X_i^{play}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;{\mathrm{\&epsiv;}}_{play}$

Rand is random number between (0,1), ε _playfor looking around step-length;

Step3.3: calculate next current location

$\left\{\begin{array}{ccc}{X}_i^{self}\left(t\right)=X_i\left(t\right)+rand\&CenterDot;\frac{X_i^{play}\left(t\right)-X_i\left(t\right)}{\left|\right|X_i^{play}\left(t\right)-X_i\left(t\right)\left|\right|}\&CenterDot;{\mathrm{\&epsiv;}}_{step}& if& f\left(X_i^{play}\left(t\right)\right)<f\left(X_i\left(t\right)\right)\\ X_i^{self}\left(t\right)=X_i\left(t\right)& if& f\left(X_i^{play}\left(t\right)\right)\&GreaterEqual;f\left(X_i\left(t\right)\right)\end{array}\right.$

Rand is random number between (0,1), ε _stepfor inertia step-length;

Step3.4:l=l+1, if l < is N _l, go to Step3.2; Otherwise, go to Step4.

5. method as claimed in claim 1 or 2, is characterized in that, described u _ssexpression formula be u _ss=0.8H (t)+0.17H (t-40)+1.12H (t-80), wherein H (t) is He Wei Saden step function; Expression is:

$H\left(t\right)=\left\{\begin{array}{c}0,t<0\\ 0.5,t=0\\ 1,t>0\end{array}\right..$

6. method as claimed in claim 4, is characterized in that, ε in Step3.2 _play=0.1||B _u-B _l||.

7. method as claimed in claim 4, is characterized in that, ε in Step3.3 _step=0.2||B _u-B _l||.