定义变量t，已知A,W,M,Thet,C,f=M*exp(t*A)*cos(t*W+Thet)+C,t1是f最小值时对应的t。dw/dt=(0.248*w-1.16*w-50*df/dt-20*(1-f))/(2.1*0.875),Pwe=97.81*w^3,5*dPg/dt+Pg=-(100/6.7)*(1-f)。当t=t2时，Pun=30-Pg-Pwe，fsecmax=1-Pun*exp(A)-(f(t2)-Pun*M).寻找最优的t2，使fsecmax最小，其中t2>t1,w大于1.47，小于2.52.分别使用yalmip和灰狼算法编程

使用YALMIP求解：

% 定义变量
t = sdpvar(1);
w = sdpvar(1);

% 定义常量
A = 0.1;
W = 0.2;
M = 0.3;
Thet = 0.4;
C = 0.5;

% 定义表达式
f = M*exp(t*A)*cos(t*W+Thet)+C;
t1 = argmin(f, t);

dw_dt = (0.248*w-1.16*w-50*derivative(f, t)-20*(1-f))/(2.1*0.875);
Pwe = 97.81*w^3;
Pg = sdpvar(1);
dPg_dt = derivative(Pg, t);
Pun = 30-Pg-Pwe;
fsecmax = 1-Pun*exp(A)-(f-Pun*M);

% 定义约束条件
constr = [t2>t1, w>1.47, w<2.52];

% 定义目标函数
obj = fsecmax;

% 求解最优解
optimize(constr, obj);

% 输出结果
disp(value(t2));

使用灰狼算法求解：

% 定义目标函数
fun = @(x) obj_func(x(1), x(2));

% 定义变量的范围
lb = [t1, 1.47];
ub = [2.52, 2.52];

% 使用灰狼算法求解最优解
[x, fval] = grey_wolf_optimizer(fun, lb, ub);

% 输出结果
disp(x(1));

其中，`obj_func`函数的定义如下：

function f = obj_func(t2, w)
    % 定义常量
    A = 0.1;
    W = 0.2;
    M = 0.3;
    Thet = 0.4;
    C = 0.5;

    % 定义表达式
    f = M*exp(t2*A)*cos(t2*W+Thet)+C;
    t1 = argmin(f, t2);

    dw_dt = (0.248*w-1.16*w-50*derivative(f, t2)-20*(1-f))/(2.1*0.875);
    Pwe = 97.81*w^3;
    Pg = -5*dPg_dt/(6.7-5*dPg_dt) * (1-f);
    dPg_dt = derivative(Pg, t2);
    Pun = 30-Pg-Pwe;
    fsecmax = 1-Pun*exp(A)-(f-Pun*M);
    f = fsecmax;
end