/
storage_model_10_layers.py
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/
storage_model_10_layers.py
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import pylab as pl
import pandas as pd
import numpy as np
import casadi as ca
import casiopeia as cp
import matplotlib.pyplot as plt
#############################
datatable = "data2017-06-19"
#############################
# Constants
cp_water = 4182.0
layer = 10.0
Tamb = 20.0
alpha_0 = 1.15482
alpha_2 = 0.812026
alpha_3 = 0.523315
alpha_1 = 2.89951
alpha_0_1 = 0.827681
alpha_0_2 = 1.02636
alpha_2_1 = 1.19537
alpha_2_2 = 1.0
alpha_3_1 = 1.0
alpha_3_2 = 1.0
# States
x = ca.MX.sym("x", 10)
TSH0 = x[0]
TSH2 = x[1]
TSH3 = x[2]
TSH1 = x[3]
TSH0_1 = x[4]
TSH0_2 = x[5]
TSH2_1 = x[6]
TSH2_2 = x[7]
TSH3_1 = x[8]
TSH3_2 = x[9]
# Parameters
p = ca.MX.sym("p", 1)
alpha_iso = p[0]
# Controls
u = ca.MX.sym("u", 14)
V_PSOS = u[0]
msto = u[1]
m0minus = u[2]
m0plus = u[3]
m2minus = u[4]
m2plus = u[5]
m3minus = u[6]
m3plus = u[7]
TSOS = u[8]
TRF = u[9]
VSHP_OP = u[10]
VSHP_CL = u[11]
VSHS_OP = u[12]
VSHS_CL = u[13]
m = 2000.0 / layer
# Massflows storage
#=================================================================================================================================================
#first Layer
dotT0 = 1.0/m * (V_PSOS * TSOS - msto * TSH0 - (m0plus + V_PSOS - msto) * TSH0 + m0plus * TSH0_1 - (alpha_0 * (TSH0 - Tamb)) / cp_water) + alpha_iso
#m0minus = (m0plus + V_PSOS - msto)
#layer1.1
dotT0_1 = 1.0/m * ((m0plus + V_PSOS - msto) * TSH0 - m0plus * TSH0_1 - (m0plus + V_PSOS - msto) * TSH0_1 + m0plus * TSH0_2 \
- (alpha_0_1 * (TSH0_1 - Tamb)) / cp_water)
#layer1.2
dotT0_2 = 1.0/m * ((m0plus + V_PSOS - msto) * TSH0_1 - m0plus * TSH0_2 - (m0plus + V_PSOS - msto) * TSH0_2 + m0plus * TSH2 \
- (alpha_0_2 * (TSH0_2 - Tamb)) / cp_water)
#second Layer
dotT2 = 1.0/m * ( -V_PSOS * VSHP_OP * TSH2 + msto * VSHS_OP * TRF + (m0plus + V_PSOS - msto) * TSH0_2 - m0plus * TSH2 \
- (-V_PSOS * VSHP_OP + V_PSOS - msto + msto * VSHS_OP + m2plus) * TSH2 + m2plus * TSH2_1 - (alpha_2 * (TSH2 - Tamb)) / cp_water)
#m2minus = (-V_PSOS*VSHP_OP +V_PSOS -msto +msto*VSHS_OP +m2plus)
#layer2.1
dotT2_1 = 1.0/m * ((-V_PSOS*VSHP_OP +V_PSOS -msto +msto*VSHS_OP +m2plus) * TSH2 - m2plus * TSH2_1 - \
(-V_PSOS*VSHP_OP +V_PSOS -msto +msto*VSHS_OP +m2plus) * TSH2_1 + m2plus * TSH2_2 - alpha_2_1 * (TSH2_1 - Tamb) / cp_water)
#layer2.2
dotT2_2 = 1.0/m * ((-V_PSOS*VSHP_OP +V_PSOS -msto +msto*VSHS_OP +m2plus) * TSH2_1 - m2plus * TSH2_2 - \
(-V_PSOS*VSHP_OP +V_PSOS -msto +msto*VSHS_OP +m2plus) * TSH2_2 + m2plus * TSH3 - alpha_2_2 * (TSH2_2 - Tamb) / cp_water)
#third Layer
dotT3 = 1.0/m * ((-V_PSOS * VSHP_OP + V_PSOS - msto + msto*VSHS_OP + m2plus) * TSH2_2 - m2plus * TSH3 \
- (-V_PSOS * VSHP_OP + V_PSOS - msto + msto * VSHS_OP + m2plus) * TSH3 + m2plus * TSH3_1 - (alpha_3 * (TSH3 - Tamb)) / cp_water)
#m3minus = (-V_PSOS*VSHP_OP +V_PSOS -msto +msto*VSHS_OP +m3plus)## m3minus ist m2minus und m3plus ist m2plus
#leyer3.1
dotT3_1 = 1.0/m * ((-V_PSOS*VSHP_OP +V_PSOS -msto +msto*VSHS_OP +m2plus) * TSH3 - m2plus * TSH3_1 \
- (-V_PSOS*VSHP_OP +V_PSOS -msto +msto*VSHS_OP +m2plus) * TSH3_1 + m2plus * TSH3_2 - (alpha_3_1 * (TSH3_1 - Tamb)) / cp_water)
#leyer3.2
dotT3_2 = 1.0/m * ((-V_PSOS*VSHP_OP +V_PSOS -msto +msto*VSHS_OP +m2plus) * TSH3_1 - m2plus * TSH3_2 \
- (-V_PSOS*VSHP_OP +V_PSOS -msto +msto*VSHS_OP +m2plus) * TSH3_2 + m2plus * TSH1 - (alpha_3_2 * (TSH3_2 - Tamb)) / cp_water)
#fourth Layer
dotT1 = 1.0/m * (-V_PSOS * VSHP_CL * TSH1 + (-V_PSOS * VSHP_OP + V_PSOS - msto + msto * VSHS_OP + m2plus) * TSH3_2 \
- m2plus * TSH1 + msto * VSHS_CL * TRF - (alpha_1 * (TSH1 - Tamb)) / cp_water)
#=================================================================================================================================================
#ODE
f = ca.vertcat([ \
dotT0, \
dotT2, \
dotT3, \
dotT1, \
dotT0_1,\
dotT0_2,\
dotT2_1,\
dotT2_2,\
dotT3_1,\
dotT3_2])
phi = x
system = cp.system.System(x = x, u = u, f = f, phi = phi, p = p)
# Start heating
int_start =0
int_end = 86000
# int_step = 1
data = pd.read_table("data-ausgewertet/10_layer/"+ datatable + ".csv", \
delimiter=",", index_col=0)
time_points = data["time"].values[int_start:]
udata_0 = data["V_PSOS"][:-1].values[int_start:]
udata_1 = data["msto"][:-1].values[int_start:]
udata_2 = data["m0minus"][:-1].values[int_start:]
udata_3 = data["m0plus"][:-1].values[int_start:]
udata_4 = data["m2minus"][:-1].values[int_start:]
udata_5 = data["m2plus"][:-1].values[int_start:]
udata_6 = data["m3minus"][:-1].values[int_start:]
udata_7 = data["m3plus"][:-1].values[int_start:]
udata_8 = data["TSOS"][:-1].values[int_start:]
udata_9 = data["TRF"][:-1].values[int_start:]
udata_10 = data["VSHP_OP"][:-1].values[int_start:]
udata_11 = data["VSHP_CL"][:-1].values[int_start:]
udata_12 = data["VSHS_OP"][:-1].values[int_start:]
udata_13 = data["VSHS_CL"][:-1].values[int_start:]
udata = ca.horzcat([udata_0, udata_1, udata_2, udata_3, udata_4, udata_5, udata_6, udata_7, udata_8, udata_9, \
udata_10, udata_11, udata_12, udata_13])
x0_init = data["TSH0"].values[int_start]
x1_init = data["TSH2"].values[int_start]
x2_init = data["TSH3"].values[int_start]
x3_init = data["TSH1"].values[int_start]
x4_init = data["TSH0_1"].values[int_start]
x5_init = data["TSH0_2"].values[int_start]
x6_init = data["TSH2_1"].values[int_start]
x7_init = data["TSH2_2"].values[int_start]
x8_init = data["TSH3_1"].values[int_start]
x9_init = data["TSH3_2"].values[int_start]
xinit = ca.horzcat([pl.atleast_2d(x0_init).T, pl.atleast_2d(x1_init).T, pl.atleast_2d(x2_init).T, pl.atleast_2d(x3_init).T, \
pl.atleast_2d(x4_init).T, pl.atleast_2d(x5_init).T, pl.atleast_2d(x6_init).T, pl.atleast_2d(x7_init).T, pl.atleast_2d(x8_init).T, \
pl.atleast_2d(x9_init).T,])
# mpe = cp.pe.MultiLSq(pe_setups)
# # # mpe.run_parameter_estimation({"linear_solver": "ma57"})
# mpe.run_parameter_estimation()
sim_est = cp.sim.Simulation(system = system, pdata = 0.0)
# sim_est = cp.sim.Simulation(system = system, pdata = mpe.estimated_parameters)
sim_est.run_system_simulation(time_points = time_points, \
x0 = xinit[0,:], udata = udata)
pl.close("all")
# # # Plot
pl.figure(figsize= (20,14))
# pl.subplot(2, 1, 1)
pl.subplot2grid((3, 1), (0, 0), rowspan=2)
pl.scatter(time_points[::500], data["TSH0"].values[int_start::500], marker = "x", label = r"meas TSH0", color = "b")
pl.scatter(time_points[::500], data["TSH2"].values[int_start::500], marker = "x", label = r"meas TSH2", color = "g")
pl.scatter(time_points[::500], data["TSH3"].values[int_start::500], marker = "x", label = r"meas TSH3", color = "r")
pl.scatter(time_points[::500], data["TSH1"].values[int_start::500], marker = "x", label = r"meas TSH1", color = "c")
pl.plot(time_points, pl.squeeze(sim_est.simulation_results[0,:]), label = r"sim TSH0", color = "b")
pl.plot(time_points, pl.squeeze(sim_est.simulation_results[1,:]), label = r"sim TSH2", color = "g")
pl.plot(time_points, pl.squeeze(sim_est.simulation_results[2,:]), label = r"sim TSH3", color = "r")
pl.plot(time_points, pl.squeeze(sim_est.simulation_results[3,:]), label = r"sim TSH1", color = "c")
pl.plot(time_points[::500], data["TSOS"].values[int_start::500], label = r"meas TSOS", color = "darkorange")
pl.title("storage model")
pl.ylabel('temperature (C)')
pl.xlabel('time (s)')
pl.legend(loc = "upper left")
pl.xlim([time_points[0], int_end])
pl.title("Scenario: " + datatable , y=1.08)
pl.subplot2grid((3, 1), (2, 0))
pl.plot( data["V_PSOS"], label = "m_PSOS")
pl.plot( data["msto"], label = "msto")
pl.xlabel('time (s)')
pl.ylabel('massflow (kg/s)')
pl.xlim([time_points[0], int_end])
pl.legend(loc = "upper left")
pl.savefig("/home/da/Master/Thesis/Optimal-Control-Storage/plots_pe/10_schichten/" + str(datatable) + "_" \
+ "start_from_" + str(int_start) + "_" \
#+ str(int_end)+\
"storage.png", \
bbox_inches='tight')
pl.show()