# -*- coding: utf-8 -*- """ Created on Thu Apr 27 19:09:02 2023 数据类 @author: 林梦珂 """ import pandas as pd import numpy as np class dataStructure(): def __init__(self):#year是输入的第几年参数，从0开始 #%%常数设置 self.name=['xw','nzd'] #时段index self.M=range(12)#一年12月 # self.T=range(684)#长系列月时段数，57年684 # self.L_T=range(685)#长系列水位库容 # self.KK1=np.arange(1,685,1) self.T=range(12)#长系列月时段数 self.L_T=range(13)#长系列水位库容 self.KK1=np.arange(1,13,1) self.delta_t=730#月小时数 self.TC=10000#通道约束 self.zdl={'xw':1166,'nzd':765} self.znl={'xw':1240,'nzd':812} self.zfl={'xw':1236,'nzd':804} self.qmax={'xw':15666,'nzd':27418} self.qmin={'xw':123,'nzd':174} #self.qmin={'xw':324,'nzd':233} self.qpmax={'xw':2340,'nzd':3537} self.qpmin={'xw':0,'nzd':0} self.rni={'xw':4200,'nzd':5850} # self.rnf={'xw':1000,'nzd':1700} self.rnf_system=2500#水风光保证出力 self.refill_level={'xw':1240,'nzd':812} # self.rnf=[800,900,1000,1100,1000,1100,1400,1000,1100,1100,1200,900]#顶峰8小时 # self.Nmax=[8000,8000,8000,8000,8000,8000,8000,8000,8000,8000,8000,8000]#模拟提供的梯级最大出力，顶峰8小时 # self.rnf=[600,800,200,200,300,300,600,400,300,300,400,500]#顶峰4小时 # self.Nmax=[8000,8000,8000,8000,9000,9000,9000,9000,9000,9000,9000,9000]#顶峰4小时 # self.rnf=[600,900,400,400,500,500,900,600,600,500,500,600]#顶峰6小时 # self.Nmax=[8000,8000,8000,8000,9000,8000,9000,9000,9000,8000,9000,9000]#顶峰6小时 # self.rnf=[600,900,600,800,800,900,1200,900,900,800,800,700]#顶峰8小时 # self.Nmax=[8000,8000,8000,8000,8000,8000,8000,8000,8000,8000,8000,8000]#顶峰8小时 self.rnf=[800,900,1000,1300,1300,1400,1400,1200,1300,1300,1100,1000]#顶峰10小时 self.Nmax=[8000,8000,8000,8000,8000,8000,8000,8000,8000,8000,8000,8000]#顶峰10小时 # self.rnf=[1000,1000,1400,1800,1900,1900,1800,1600,1700,1400,1400,1300]#顶峰12小时 # self.Nmax=[8000,8000,8000,8000,8000,8000,8000,8000,8000,8000,8000,8000]#顶峰12小时 self.Q1_dis=[0,1240,2340]##发电流量能力离散 self.Q2_dis=[0,1800,3537] self.head_xw=[159,214,251] self.head_nzd=[168,210,254] #%%径流、月尺度风光 # self.wp_data_t=np.array(pd.read_excel('多年入流&风光_训练.xlsx','wp')['风光'])#月尺度新能源电量数据 # self.runoff_data_xw=np.array(pd.read_excel('多年入流&风光_训练.xlsx','xw')['入流']) # self.runoff_data_nzd=np.array(pd.read_excel('多年入流&风光_训练.xlsx','nzd')['入流']) self.wp_data_t=np.array(pd.read_excel('G:/Spyder_workspace/2. Operation rules/Basic data/丰平枯典型年径流&风光.xlsx','wet')['wp'])#月尺度新能源电量数据 self.runoff_data_xw=np.array(pd.read_excel('G:/Spyder_workspace/2. Operation rules/Basic data/丰平枯典型年径流&风光.xlsx','wet')['xw']) self.runoff_data_nzd=np.array(pd.read_excel('G:/Spyder_workspace/2. Operation rules/Basic data/丰平枯典型年径流&风光.xlsx','wet')['nzd']) #%%各电站特性曲线读取、Epeak filename='G:/Spyder_workspace/2. Operation rules/Basic data/HydroStations_info.xlsx' #库容、尾水位曲线 self.z_up1=pd.read_excel(filename,sheet_name='z_v1')['z_up'] self.v1=pd.read_excel(filename,sheet_name='z_v1')['v'] self.z_down1=pd.read_excel(filename,sheet_name='z_q1')['z_down'] self.q1=pd.read_excel(filename,sheet_name='z_q1')['q'] self.z_up2=pd.read_excel(filename,sheet_name='z_v2')['z_up'] self.v2=pd.read_excel(filename,sheet_name='z_v2')['v'] self.z_down2=pd.read_excel(filename,sheet_name='z_q2')['z_down'] self.q2=pd.read_excel(filename,sheet_name='z_q2')['q'] ##水头耗水率曲线 self.head1=pd.read_excel(filename,sheet_name='head_rate1')['head'] self.warate1=pd.read_excel(filename,sheet_name='head_rate1')['rate'] self.head2=pd.read_excel(filename,sheet_name='head_rate2')['head'] self.warate2=pd.read_excel(filename,sheet_name='head_rate2')['rate'] #Epeak各月曲线 filename2='G:/Spyder_workspace/2. Operation rules/Basic data/Epeak_curves_4h.xlsx' N={} Epeak={} for i in range(12): temp_N=np.array(pd.read_excel(filename2,sheet_name='%d月'%(i+1))['N']) temp_Epeak=np.array(pd.read_excel(filename2,sheet_name='%d月'%(i+1))['Epeak']) N.update({i:temp_N}) Epeak.update({i:temp_Epeak}) self.N=N self.Epeak=Epeak # #NHQ 数据读取 self.nhq_data_xw=pd.read_excel(filename,sheet_name='NHQ1') self.nhq_data_nzd=pd.read_excel(filename,sheet_name='NHQ2') self.nhq_q_xw=dataStructure.nhq_creat(self.nhq_data_xw)[0] self.nhq_n_xw=dataStructure.nhq_creat(self.nhq_data_xw)[1] self.nhq_q_nzd=dataStructure.nhq_creat(self.nhq_data_nzd)[0] self.nhq_n_nzd=dataStructure.nhq_creat(self.nhq_data_nzd)[1] # #耗水率三维对应N self.QP1=[] self.Lev1=[] self.N1=[] self.QP2=[] self.Lev2=[] self.N2=[] for i in range(len(self.Q1_dis)-1): k=len(self.head1)-1 for j in range(k): self.QP1.append([self.Q1_dis[i],self.Q1_dis[i+1],self.Q1_dis[i+1]]) self.QP1.append([self.Q1_dis[i+1],self.Q1_dis[i],self.Q1_dis[i]]) self.Lev1.append([self.head1[k-j],self.head1[k-j],self.head1[k-j-1]]) self.Lev1.append([self.head1[k-j-1],self.head1[k-j-1],self.head1[k-j]]) self.N1.append([self.Q1_dis[i]/self.warate1[k-j]*3.6,self.Q1_dis[i+1]/self.warate1[k-j]*3.6,self.Q1_dis[i+1]/self.warate1[k-j-1]*3.6]) self.N1.append([self.Q1_dis[i+1]/self.warate1[k-j-1]*3.6,self.Q1_dis[i]/self.warate1[k-j-1]*3.6,self.Q1_dis[i]/self.warate1[k-j]*3.6]) for i in range(len(self.Q2_dis)-1): k=len(self.head2)-1 for j in range(k): self.QP2.append([self.Q2_dis[i],self.Q2_dis[i+1],self.Q2_dis[i+1]]) self.QP2.append([self.Q2_dis[i+1],self.Q2_dis[i],self.Q2_dis[i]]) self.Lev2.append([self.head2[k-j],self.head2[k-j],self.head2[k-j-1]]) self.Lev2.append([self.head2[k-j-1],self.head2[k-j-1],self.head2[k-j]]) self.N2.append([self.Q2_dis[i]/self.warate2[k-j]*3.6,self.Q2_dis[i+1]/self.warate2[k-j]*3.6,self.Q2_dis[i+1]/self.warate2[k-j-1]*3.6]) self.N2.append([self.Q2_dis[i+1]/self.warate2[k-j-1]*3.6,self.Q2_dis[i]/self.warate2[k-j-1]*3.6,self.Q2_dis[i]/self.warate2[k-j]*3.6]) #%%库容曲线及尾水位曲线的index self.M1=np.arange(1,len(self.z_up1),1)#小湾库水位01变量索引 self.J1=np.arange(1,len(self.z_down1),1)#小湾尾水位01变量索引 self.M2=np.arange(1,len(self.z_up2),1)#糯扎渡库水位01变量索引 self.J2=np.arange(1,len(self.z_down2),1)#糯扎渡尾水位 self.W1=np.arange(1,len(self.head1),1)#小湾水头耗水率01变量索引 # self.U1=np.arange(1,len(self.warate1),1)#小湾耗水率 self.W2=np.arange(1,len(self.head2),1)#糯扎渡水头耗水率01变量索引 # self.U2=np.arange(1,len(self.warate2),1)#糯扎渡耗水率 self.C1=np.arange(1,len(self.nhq_q_xw),1)#nhq q离散区间数量 self.C2=np.arange(1,len(self.nhq_q_nzd),1)#nhq q离散区间数量 self.B1=np.arange(1,len(self.head_xw),1)#nhq h离散区间个数 self.B2=np.arange(1,len(self.head_nzd),1)#nhq h离散区间个数 self.EP=np.arange(1,len(self.N[0]),1)#Epeakcurve 01变量索引 self.F1=range((len(self.Q1_dis)-1)*(len(self.warate1)-1)*2) self.F2=range((len(self.Q2_dis)-1)*(len(self.warate2)-1)*2) self.TR=range(3)#三角形点数，3 #%%方法 #读取场景数据，并赋值给dataclass变量的方法 def scenarios_dis(data):#length 是场景长度 res={}#最终场景结果，字典表示 for i in range(1,13): temp=data[data['月份']==i] temp=temp.drop('月份',axis=1) res.update({i:np.array(temp)}) return res #给定日电量，按照新能源日内特征曲线进行缩放 def get_wpi(E,typicalcurves,TC): results=np.zeros((4,24)) k=0 for curve in typicalcurves: curve=np.array(curve) output=np.array(E/np.mean(curve)*curve) output[output>TC]=TC results[k]=output k+=1 return results def nhq_creat(data):#data是输入的数据，第一列是Q,后面列是不同水头下的出力 Q=data.iloc[:,0].values N=(data.drop('Q',axis=1).values).T return Q,N if __name__=='__main__': ds=dataStructure()