#### Simulation: ####
N_sim = T_horiz
# Time vector
k_range = np.arange(N_sim)
k_range_x = np.arange(N_sim + 1)
# State variables
E = np.zeros(N_sim + 1)
E[0] = E_rated / 2
# Control variables
P_sto = np.zeros(N_sim)
# Simulation loop:
for k in k_range:
# Control computation:
P_sto_law = dpsolv.interp_on_state(pol_sto[k])
P_sto[k] = P_sto_law(E[k])
# State evolution:
E[k + 1], = sto_sys.dyn(k, E[k], P_sto[k])
# Compute state variables derivatives:
E_full = np.ma.array(E, mask=(E < E_rated * 0.9999))
E_empty = np.ma.array(E, mask=(E > E_rated * 0.0001))
# Deviation from commitment:
P_grid = P_prod_data - P_sto
P_grid_l2 = np.sqrt(np.mean(P_grid**2))
print('RMS deviation: {:.4f}'.format(P_grid_l2))
### Plot:
fig, ax = plt.subplots(2, 1, sharex=True)
N_sim = T_horiz
# Time vector
k_range = np.arange(N_sim)
k_range_x= np.arange(N_sim+1)
# State variables
E = np.zeros(N_sim+1)
E[0] = p['E_rated']/2
# Control variables
P_sto = np.zeros(N_sim)
# Simulation loop:
for k in k_range:
# Control computation:
P_sto_law = dpsolv.interp_on_state(pol_sto[k])
P_sto[k] = P_sto_law(E[k])
# State evolution:
E[k+1], = sto_sys.dyn(k, E[k], P_sto[k])
# Compute state variables derivatives:
E_full = np.ma.array(E, mask = (E<p['E_rated']*0.9999))
E_empty = np.ma.array(E, mask = (E>p['E_rated']*0.0001))
# Deviation from commitment:
P_dev = p['P_req_data'] - P_sto
P_req_l2 = np.sqrt(np.mean(p['P_req_data']**2))
P_dev_l2 = np.sqrt(np.mean(P_dev**2))
print('input RMS deviation: {:.4f}'.format(P_req_l2))
print('output RMS deviation: {:.4f}'.format(P_dev_l2))
