如何解决用于预冷的 cvxpy 优化?
#PRE COOLING FOR REAL TIME PRICE BASED ON EXPANDED TEMPERATURE SETPOINT (s)
Rate_s = (df_jul1rate['Rate']) #real time price (RTP)
C_reg_s = pd.Series(df_summerloads['Cooling end-use energy']) #load profile for cooling
C_precool = cp.Variable(len(C_reg_s))
Cost_reg_s = sum(C_reg_s*Rate_s)
print(sum(C_reg_s)) #kwh/day cooling uncontrolled for RTP
print(Cost_reg_s) #cost/day cooling uncontrolled for RTP
s = 10 #pre-cooling/heating limit in kwh
hours = 24
Shift = pd.Series([s]*hours)
Cost_opt_s = (sum(cp.multiply(C_precool,Rate_s)))
objective = cp.Minimize(Cost_opt_s)
constraints = []
for i in range (0,len(C_reg_s)):
constraints += [
C_precool[i] <= C_reg_s[i] + Shift[i],#pre cooling
C_precool[i] >= C_reg_s[i] - Shift[i],#delay cooling
C_precool[i] >= 0,sum(C_precool) == sum(C_reg_s)] #optimal delayed/pre cooling must equal regular cooling
prob = cp.Problem(objective,constraints)
prob.solve
print(sum(C_precool))
print(C_precool.value)
print(Cost_opt_s.value)
代码返回以下值,而不是针对每个时间步的最小值进行优化。 Rate_s 变量是一系列成本值。有人可以帮忙吗?
var0[0] + var0[1] + var0[2] + var0[3] + var0[4] + var0[5] + var0[6] + var0[7] + var0[8] + var0[ 9] + var0[10] + var0[11] + var0[12] + var0[13] + var0[14] + var0[15] + var0[16] + var0[17] + var0[18] + var0[ 19] + var0[20] + var0[21] + var0[22] + var0[23] 没有任何 无
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