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Reformatted plots and reset carbon tax default to 0 #3

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Reformatted plots and reset carbon tax default to 0 #3

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34 changes: 22 additions & 12 deletions watertap/examples/flowsheets/RO_multiperiod_model/pricetaker_multiperiod_RO.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,7 +69,7 @@ def _get_lmp(time_steps, data_path):
lmp_data = np.genfromtxt(data_path, delimiter=",")

# index only the desired number of timesteps
return lmp_data[:time_steps]
return lmp_data[:time_steps] * 3


def build_flowsheet(n_steps):
Expand All @@ -79,7 +79,7 @@ def build_flowsheet(n_steps):
return mp_swro


def set_objective(mp_swro, lmp, carbontax=3.5):
def set_objective(mp_swro, lmp, carbontax=0):
# Retrieve pyomo model and active process blocks (i.e. time blocks)
m = mp_swro.pyomo_model
t_blocks = mp_swro.get_active_process_blocks()
Expand All @@ -105,6 +105,7 @@ def set_objective(mp_swro, lmp, carbontax=3.5):

# set the electricity_price in each flowsheet
blk_swro.fs.costing.electricity_cost.fix(blk.lmp_signal)
blk_swro.fs.costing.utilization_factor.fix(1)

# combine/place flowsheet level cost metrics on each time block
blk.weighted_LCOW = Expression(
Expand Down Expand Up @@ -209,28 +210,37 @@ def visualize_results(m, t_blocks, data):
ax[0, 0].set_xlabel("Time [hr]")
ax[0, 0].set_ylabel("Electricity price [$/kWh]")

ax[1, 0].plot(time_step, pump1_flow * 3600, label="P1")
ax[1, 0].plot(time_step, pump2_flow * 3600, label="P2")
ax[1, 0].plot(time_step, 100 * pump1_flow / max(pump1_flow), label="Main pump")
ax[1, 0].plot(time_step, 100 * pump2_flow / max(pump2_flow), label="Boost pump")
ax[1, 0].set_xlabel("Time [hr]")
ax[1, 0].set_ylabel("Pump flowrate [m3/hr]")
ax[1, 0].set_ylabel("Pump flowrate [% of max]")
ax[1, 0].set_ylim([0, 100])

ax[2, 0].plot(time_step, recovery * 100)
ax[2, 0].set_xlabel("Time [hr]")
ax[2, 0].set_ylabel("Water Recovery [%]")
ax[2, 0].set_ylim([1, 100])

ax[0, 1].plot(time_step, power)
ax[0, 1].plot(time_step, 100 * power / max(power))
ax[0, 1].set_xlabel("Time [hr]")
ax[0, 1].set_ylabel("Net Power [kWh]")
ax[0, 1].set_ylabel("Net Power [% of max power]")
ax[0, 1].set_ylim([0, 100])

ax[1, 1].plot(time_step, pump1_efficiency * 100, label="P1")
ax[1, 1].plot(time_step, pump2_efficiency * 100, label="P2")
ax[1, 1].plot(
time_step, 100 * pump1_efficiency / max(pump1_efficiency), label="Main pump"
)
ax[1, 1].plot(
time_step, 100 * pump2_efficiency / max(pump2_efficiency), label="Boost pump"
)
ax[1, 1].set_xlabel("Time [hr]")
ax[1, 1].set_ylabel("Pump efficiency [%]")
ax[1, 1].set_ylabel("Pump efficiency [% of max efficiency]")
ax[1, 1].legend()
ax[1, 1].set_ylim([0, 100])

ax[2, 1].plot(time_step, pressure * 1e-5)
ax[2, 1].plot(time_step, 100 * pressure / (80e5))

@adam-a-a adam-a-a Aug 26, 2022

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@arao53 With regard to the pressure, now that I think about it, the capital cost of the pump will vary by pressure and flow rate (essentially power consumption) unless we specify that the pump should be costed by the design pressure and flow rate of the pump. Not sure if that's the case

ax[2, 1].set_xlabel("Time [hr]")
ax[2, 1].set_ylabel("RO Inlet Pressure [bar]")
ax[2, 1].set_ylabel("RO Inlet Pressure [% of max pressure]")
ax[2, 1].set_ylim([0, 100])


if __name__ == "__main__":
Expand Down