def setUp(self):
# calculate stage pressures
cmp_p0 = 101.3 # kPa
cmp_p2 = 10.0e3 # kPa
cmp_p1 = (1.43 * cmp_p0 * cmp_p2)**0.5 # kPa
exp_p0 = 7.0e3 # kPa
exp_p2 = 101.3 # kPa
exp_p1 = (1.43 * exp_p0 * exp_p2)**0.5 # kPa
# test conditions
T_atm_K = 293.15
T_atm_C = T_atm_K - 273.15
inputs = ICAES.get_default_inputs()
inputs['p_atm'] = cmp_p0 # kPa
inputs['T_atm'] = T_atm_C # C
inputs['depth'] = 900.0 # [m]
inputs['PR_type'] = 'fixed'
# inputs['n_stages_cmp'] = 2
inputs['PR_cmp'] = [cmp_p1 / cmp_p0, cmp_p2 / cmp_p1]
inputs['ML_cmp1'] = 1.0 * (1.65 / (cmp_p1 / 1000) - 0.05)
inputs['ML_cmp2'] = 5.0 * (1.65 / (cmp_p2 / 1000) - 0.05)
inputs['ML_cmp3'] = -1.0
# inputs['n_stages_exp'] = 2
inputs['PR_exp'] = [exp_p0 / exp_p1, exp_p1 / exp_p2]
inputs['ML_exp1'] = 5.0 * (1.65 / (exp_p0 / 1000) - 0.05)
inputs['ML_exp2'] = 1.0 * (1.65 / (exp_p1 / 1000) - 0.05)
inputs['ML_exp3'] = -1.0
self.sys = ICAES(inputs=inputs)
# overwrite calculated air and water properties
self.sys.M = 29.0
self.sys.cp = 1.004
self.sys.gamma = 1.4
self.sys.c_water = 4.1816
self.sys.T_store_init = T_atm_K # [K]
self.sys.T_store = T_atm_K # [K]
self.sys.T2 = T_atm_K # [K]
self.sys.T3 = T_atm_K # [K]
self.sys.m_store = self.sys.m_store * 1.2 # increase amount of air to stay within pressure limits
# solve two timesteps (index 0 = compression, index 1 = expansion)
self.sys.update(m_dot=10.0, delta_t=2.0)
self.sys.update(m_dot=-10.0, delta_t=1.0)
self.sys.data.to_csv('test_perf.csv')
def parameter_sweep(sensitivity_input):
start = time.time()
# create system
inputs = ICAES.get_default_inputs()
for variable in sensitivity_input.index:
inputs[variable] = sensitivity_input[variable]
system = ICAES(inputs=inputs)
# run single cycle and analyze
system.single_cycle()
results = system.analyze_performance()
end = time.time()
results['solve_time'] = end - start
# combine inputs and results to return in single series
single_output = pd.concat([sensitivity_input, results])
return single_output
def parameter_sweep(sweep_input):
start = time.time()
# create system
inputs = ICAES.get_default_inputs()
inputs['steps'] = sweep_input['steps'] # [kPa]
inputs['p_store_min'] = sweep_input['p_min'] # [kPa]
inputs['p_store_init'] = sweep_input['p_min'] # [kPa]
inputs['p_store_max'] = sweep_input['p_max'] # [kPa]
inputs['eta_storage'] = sweep_input['eta_storage'] # [-]
inputs['V_res'] = sweep_input['V_res'] # [m3]
inputs['phi'] = sweep_input['phi'] # porosity [-]
inputs['Slr'] = sweep_input['Slr'] # liquid residual fraction [-]
system = ICAES(inputs=inputs)
# run single cycle and analyze
system.single_cycle()
results = system.analyze_performance()
end = time.time()
results['solve_time'] = end - start
# combine inputs and results to return in single series
single_output = pd.concat([sweep_input, results])
return single_output
from caes import ICAES, plot_series
import matplotlib.pyplot as plt
# create system
inputs = ICAES.get_default_inputs()
inputs['depth'] = 5550.0
system = ICAES(inputs=inputs)
# system.debug_perf()
#
# # run single cycle and analyze
system.single_cycle()
results = system.analyze_performance()
results.to_csv('single_cycle_performance.csv')
system.data.to_csv('single_cycle_timeseries.csv')
print(results)
# # plot results
system.plot_overview()
system.plot_pressures()
system.plot_pressure_losses()
class TestICAES(unittest.TestCase):
def setUp(self):
# calculate stage pressures
cmp_p0 = 101.3 # kPa
cmp_p2 = 10.0e3 # kPa
cmp_p1 = (1.43 * cmp_p0 * cmp_p2)**0.5 # kPa
exp_p0 = 7.0e3 # kPa
exp_p2 = 101.3 # kPa
exp_p1 = (1.43 * exp_p0 * exp_p2)**0.5 # kPa
# test conditions
T_atm_K = 293.15
T_atm_C = T_atm_K - 273.15
inputs = ICAES.get_default_inputs()
inputs['p_atm'] = cmp_p0 # kPa
inputs['T_atm'] = T_atm_C # C
inputs['depth'] = 900.0 # [m]
inputs['PR_type'] = 'fixed'
# inputs['n_stages_cmp'] = 2
inputs['PR_cmp'] = [cmp_p1 / cmp_p0, cmp_p2 / cmp_p1]
inputs['ML_cmp1'] = 1.0 * (1.65 / (cmp_p1 / 1000) - 0.05)
inputs['ML_cmp2'] = 5.0 * (1.65 / (cmp_p2 / 1000) - 0.05)
inputs['ML_cmp3'] = -1.0
# inputs['n_stages_exp'] = 2
inputs['PR_exp'] = [exp_p0 / exp_p1, exp_p1 / exp_p2]
inputs['ML_exp1'] = 5.0 * (1.65 / (exp_p0 / 1000) - 0.05)
inputs['ML_exp2'] = 1.0 * (1.65 / (exp_p1 / 1000) - 0.05)
inputs['ML_exp3'] = -1.0
self.sys = ICAES(inputs=inputs)
# overwrite calculated air and water properties
self.sys.M = 29.0
self.sys.cp = 1.004
self.sys.gamma = 1.4
self.sys.c_water = 4.1816
self.sys.T_store_init = T_atm_K # [K]
self.sys.T_store = T_atm_K # [K]
self.sys.T2 = T_atm_K # [K]
self.sys.T3 = T_atm_K # [K]
self.sys.m_store = self.sys.m_store * 1.2 # increase amount of air to stay within pressure limits
# solve two timesteps (index 0 = compression, index 1 = expansion)
self.sys.update(m_dot=10.0, delta_t=2.0)
self.sys.update(m_dot=-10.0, delta_t=1.0)
self.sys.data.to_csv('test_perf.csv')
# self.data.to_csv('single_cycle_timeseries.csv')
# compresor tests
def test_cmp_nLP(self):
self.assertAlmostEqual(self.sys.data.loc[0, 'cmp_n0'], 1.046, places=3)
def test_cmp_nHP(self):
self.assertAlmostEqual(self.sys.data.loc[0, 'cmp_n1'], 1.092, places=3)
def test_cmp_wLP(self):
self.assertAlmostEqual(self.sys.data.loc[0, 'cmp_w_stg0'],
-219.737,
places=3)
# expander tests
def test_exp_nLP(self):
self.assertAlmostEqual(self.sys.data.loc[1, 'exp_n1'], 1.039, places=3)
def test_exp_nHP(self):
self.assertAlmostEqual(self.sys.data.loc[1, 'exp_n0'], 1.062, places=3)
def test_exp_wHP(self):
self.assertAlmostEqual(self.sys.data.loc[1, 'exp_w_stg0'],
154.024,
places=3)
savename = "sweep_soln_settings.png"
# ------------------
# integer inputs
# ------------------
ncpus = 3 # int(os.getenv('NUM_PROCS'))
# ------------------
# numpy array inputs
# ------------------
# investigated
steps = np.array([
1.0, 2.0, 3.0, 4.0, 5.0, 7.0, 8.0, 10.0, 25.0, 50.0, 75.0, 100.0,
250.0, 500.0, 750.0, 1000.0
]) # number of solution time steps [-]
# kept at default values
p_mins = np.array([ICAES.get_default_inputs()['p_store_min']
]) # minimum pressure [kPa]
PRs = np.array([
ICAES.get_default_inputs()['p_store_max'] /
ICAES.get_default_inputs()['p_store_min']
]) # pressure ratio [-]
eta_storages = np.array([ICAES.get_default_inputs()['eta_storage']
]) # storage efficiency [fr]
V_ress = np.array([ICAES.get_default_inputs()['V_res']
]) # reservoir volume [m3]
phis = np.array([ICAES.get_default_inputs()['phi']]) # porosity [-]
Slrs = np.array([ICAES.get_default_inputs()['Slr']
]) # liquid residual fraction [-]
# ==============
# run simulations
h = 62.44 # thickness [m]
phi = 0.2292 # porosity
k = 38.33 # permeability [mD]
m_dot = 530.2402606 # mass flow rate [kg/s]
r_f = 174.6289785 # formation radius [m]
r_w = 0.41 / 2.0 # well radius [m]
df = pd.DataFrame()
for i in range(9):
# get default inputs
if i == 0:
inputs = CAES.get_default_inputs()
else:
inputs = ICAES.get_default_inputs()
# Common inputs
inputs['depth'] = depth
inputs['h'] = h
inputs['phi'] = phi
inputs['k'] = k
inputs['r_f'] = r_f
inputs['r_w'] = r_w
inputs['m_dot'] = m_dot
# options to include/exclude various loss mechanisms
inputs['include_interstage_dp'] = False
inputs['include_thermal_gradient'] = False
inputs['include_air_leakage'] = False
inputs['include_aquifer_dp'] = False
1.0e5,
5.0e5,
1.0e6,
5.0e6,
1.0e7,
5.0e7,
1.0e8,
5.0e8,
1.0e9,
5.0e9,
]) # reservoir volume [m3]
PRs = np.arange(1.1, 2.1, 0.1) # pressure ratio [-]
phis = np.array([1.0]) # porosity [-]
Slrs = np.array([0.0]) # liquid residual fraction [-]
# kept at default values
steps = np.array([ICAES.get_default_inputs()['steps']
]) # number of solution time steps [-]
p_mins = np.array([ICAES.get_default_inputs()['p_store_min']
]) # minimum pressure [kPa]
eta_storages = np.array([ICAES.get_default_inputs()['eta_storage']
]) # storage efficiency [fr]
# ==============
# run simulations
# ==============
# prepare dataframe to store inputs
attributes = [
'steps', 'p_min', 'PR', 'p_max', 'eta_storage', 'V_res', 'phi', 'Slr'
]
sweep_inputs = pd.DataFrame(columns=attributes)
def parameter_sweep(sweep_input, debug=True):
start = time.time()
# convert inputs to model units
kW_out = sweep_input['capacity_MW'] * 1e3
kWh_out = sweep_input['capacity_MW'] * sweep_input['duration_hr'] * 1e3
if debug:
print('\nStarting sizing')
print("kW_out (desired) : " + str(round(kW_out, 3)))
print("kWh_out (desired) : " + str(round(kWh_out, 3)))
print("\nDepth (m) : " + str(sweep_input['depth_m']))
print("Thickness (m) : " + str(sweep_input['thickness_m']))
print("Porosity (-) : " + str(sweep_input['porosity']))
print("Permeability (mD): " + str(sweep_input['permeability_mD']))
# allowable calculation error
error = 1e-6
# maximum number of iterations
count_max = 200
# initial guesses
m_dot = 10.0
r_f = 10.0
# initial results
kW_out_actual = 0.0
kWh_out_actual = 0.0
count = 0
while abs(kW_out_actual - kW_out) / kW_out + abs(
kWh_out_actual - kWh_out) / kWh_out > error:
if debug:
print("\nIteration : " + str(count))
print("m_dot : " + str(round(m_dot, 3)))
print("r_f : " + str(round(r_f, 3)))
# create system
inputs = ICAES.get_default_inputs()
# user inputs
inputs['depth'] = sweep_input['depth_m'] # porosity depth [m]
inputs['h'] = sweep_input['thickness_m'] # porosity thickness [m]
inputs['phi'] = sweep_input['porosity'] # formation porosity [-]
inputs['k'] = sweep_input[
'permeability_mD'] # formation permeability [mD]
# current guess/iteration
inputs['m_dot'] = m_dot # [kg/s]
inputs['r_f'] = r_f # [m]
system = ICAES(inputs=inputs)
# run single cycle and analyze
system.single_cycle()
results = system.analyze_performance()
# extract results of interest
kW_out_actual = results['kW_out_avg']
kWh_out_actual = results['kWh_out']
# update guesses
tau = 0.5 # solver time constant
m_dot = m_dot * (1.0 + tau * (kW_out - kW_out_actual) / kW_out
) # m_dot linearly linked to kW
r_f = r_f * (1.0 + tau**2 * (kWh_out - kWh_out_actual) / kWh_out_actual
) # r_f exponentially linked to kWh
count = count + 1
if count > count_max: # sizing unsuccessful
results['errors'] = True
break
if debug:
print("MW_out_avg : " + str(round(kW_out_actual / 1e3, 3)))
print("MWh_out : " + str(round(kWh_out_actual / 1e3, 3)))
end = time.time()
results['solve_time'] = end - start
results['iterations'] = count
results['m_dot'] = m_dot
results['r_f'] = r_f
# print out RTE
print(results['RTE'])
# combine inputs and results to return in single series
single_output = pd.concat([sweep_input, results])
return single_output
phi = 0.2292 # porosity
k = 38.33 # permeability [mD]
m_dot = 200.0 # mass flow rate [kg/s]
r_f = 100.0 # formation radius [m]
r_w = 0.41 / 2.0 # well radius [m]
depths = [1000.0, 2000.0, 3000.0, 4000.0, 5000.0] # depth [m]
all_results = pd.DataFrame()
# iterate through depths
for depth in depths:
# iterate through test cases
for i in range(5):
inputs = ICAES.get_default_inputs()
inputs['depth'] = depth
inputs['h'] = h
inputs['phi'] = phi
inputs['k'] = k
inputs['r_f'] = r_f
inputs['r_w'] = r_w
inputs['m_dot'] = m_dot
# ===========================
# cases
# ===========================
if i == 0:
casename = 'default'
elif i == 1: # i == 1