matlab代码翻译成pythonNumG = size(Data.G,1); NumPV = size(Data.PVForecast,1); NumES = size(Data.ES,1); NumPL = size(Data.PL,1);

这段 Matlab 代码用于获取 Data.G、Data.PVForecast、Data.ES 和 Data.PL 中的行数，然后分别赋值给变量 NumG、NumPV、NumES 和 NumPL。下面是 Python 代码的翻译：

NumG = Data.G.shape[0]
NumPV = Data.PVForecast.shape[0]
NumES = Data.ES.shape[0]
NumPL = Data.PL.shape[0]

其中，`Data.G.shape` 是一个元组，包含了 `Data.G` 的行数和列数。`Data.G.shape[0]` 就是 `Data.G` 的行数，同理可得其他变量的值。

相关问题

matlab代码翻译成PythonPV = sdpvar(NumPV,OperationPeriod,'full');

这行 Matlab 代码创建了一个大小为 NumPV * OperationPeriod 的矩阵变量 PV，其元素类型为实数。在 Python 中，可以使用 cvxpy 库来实现类似的功能，代码如下：

import cvxpy as cp

PV = cp.Variable((NumPV, OperationPeriod), name='PV', 
                 pos=True, shape=(NumPV, OperationPeriod))

其中，pos=True 表示所有元素均为非负数。注意，Python 中的变量名不能与保留关键字重复，因此将变量名改为 PV。

matlab代码翻译成pythonG_Ebalance=[]; for j=1:OperationPeriod G_Ebalance=G_Ebalance+[sum(EG(:,j))+sum(Data.ESDisch(:,j)) + sum(PV(:,j)) == sum(Data.PL(:,j))+sum(Data.ESCh(:,j))]; end %% 约束2：电源出力上下限 %% G_G=[]; for i = 1 : NumG for j = 1 : OperationPeriod G_G=G_G+[Data.G(i,1)<=EG(i,j)]; end end %% 约束3：光伏出力约束 %% G_PV=[]; for i = 1 : NumPV for j = 1 : OperationPeriod if Data.PVForecast(i,j)<=Data.PL(i,j) G_PV = G_PV+[PV(i,j)==Data.PVForecast(i,j)]; else G_PV = G_PV+[PV(i,j)==Data.PL(i,j)]; end end end %% ------------------------目标函数------------------------------- %% %% 成本变量 %% CostG = 0; CostWT = 0; CostESdisch = 0; CostESch = 0; %% 电源成本 CostG = CostG+sum(EG.*Data.PriceE); %% 清洁能源成本 CostWT=CostWT+sum(sum(0.5*PV)); %% 弃光量 PVWAST = sum(sum(Data.PVForecast-PV)); %% 光电总量 PVF=0; PVF=PVF+sum(sum(Data.PVForecast)); %% 储能设备充电成本 %% CostESch = CostESch+sum(sum(ESCh).*Data.PriceE); %% 储能设备售电成本 %% CostESdisch = CostESdisch+sum(sum(ESDisch).*Data.PriceE); %% 分时成本 C2=sum(ESCh).*Data.PriceE-sum(ESDisch).*Data.PriceE; %% 总成本 %% + CostHeatBoiler + CostElectricBoiler + CO2 Cost = (CostESch-CostESdisch); G=[G_Ebalance+G_G+G_PV]; options =sdpsettings('verbose',2,'solver','CPLEX'); optimize(G,Cost,options); WYH.C2=value(C2); WYH.Cost=Cost; end

Translated code in Python:

G_Ebalance = []
for j in range(OperationPeriod):
    G_Ebalance = G_Ebalance + [sum(EG[:, j]) + sum(Data.ESDisch[:, j]) + sum(PV[:, j]) == sum(Data.PL[:, j]) + sum(Data.ESCh[:, j])]
    
G_G = []
for i in range(NumG):
    for j in range(OperationPeriod):
        G_G = G_G + [Data.G[i, 1] <= EG[i, j]]

G_PV = []
for i in range(NumPV):
    for j in range(OperationPeriod):
        if Data.PVForecast[i, j] <= Data.PL[i, j]:
            G_PV = G_PV + [PV[i, j] == Data.PVForecast[i, j]]
        else:
            G_PV = G_PV + [PV[i, j] == Data.PL[i, j]]

CostG = 0
CostWT = 0
CostESdisch = 0
CostESch = 0

CostG = CostG + sum(EG * Data.PriceE)

CostWT = CostWT + sum(sum(0.5 * PV))

PVWAST = sum(sum(Data.PVForecast - PV))

PVF = 0
PVF = PVF + sum(sum(Data.PVForecast))

CostESch = CostESch + sum(sum(ESCh) * Data.PriceE)

CostESdisch = CostESdisch + sum(sum(ESDisch) * Data.PriceE)

C2 = sum(ESCh) * Data.PriceE - sum(ESDisch) * Data.PriceE

Cost = (CostESch - CostESdisch)

G = G_Ebalance + G_G + G_PV

options = sdpsettings(verbose=2, solver='CPLEX')

optimize(G, Cost, options)

WYH.C2 = value(C2)
WYH.Cost = Cost

Note: This code translation is based on my understanding of the original code. Please make sure to confirm the correctness of the translated code before using it.