penal = (P_grid/power_max)**2
'''
P_prod = searev_power(Speed)
P_grid = P_prod - P_sto
penal = (P_grid / power_max)**2
return penal
cost_label = 'quadratic cost'
### Create the system description:
searev_sys = SysDescription((3, 1, 1), name='Searev + Storage')
searev_sys.dyn = dyn_searev_sto
searev_sys.control_box = admissible_controls
searev_sys.cost = cost_model
searev_sys.perturb_laws = [innov_law]
#searev_sys.print_summary()
### Create the DP solver:
dpsolv = DPSolver(searev_sys)
# discretize the state space
N_E = 31
N_S = 61
N_A = 61
S_min, S_max = -4 * .254, 4 * 0.254
A_min, A_max = -4 * .227, 4 * .227
x_grid = dpsolv.discretize_state(0, E_rated, N_E, S_min, S_max, N_S, A_min,
A_max, N_A)
E_grid, S_grid, A_grid = x_grid
# discretize the perturbation
return (x + u - w,)
# Attach the dynamical equation to the system description:
invsys.dyn = dyn_inv
demand_values = [0, 1, 2, 3]
demand_proba = [0.2, 0.4, 0.3, 0.1]
demand_law = stats.rv_discrete(values=(demand_values, demand_proba))
demand_law = demand_law.freeze()
demand_law.pmf([0, 3]) # Probality Mass Function
demand_law.rvs(10) # Random Variables generation
invsys.perturb_laws = [demand_law] # a list, to support several perturbations
def admissible_orders(x):
'interval of allowed orders U(x_k)'
U1 = (0, 10)
return (U1, ) # tuple, to support several controls
# Attach it to the system description.
invsys.control_box = admissible_orders
### Cost description g = r(x) + c.u
(h,p,c) = 0.5, 3, 1
def op_cost(x,u,w):
'operational cost of the shop'
holding = x*h
shortage = -x*p
'dynamical equation of the inventory stock `x`. Returns x(k+1).'
return (x + u - w, )
# Attach the dynamical equation to the system description:
invsys.dyn = dyn_inv
demand_values = [0, 1, 2, 3]
demand_proba = [0.2, 0.4, 0.3, 0.1]
demand_law = stats.rv_discrete(values=(demand_values, demand_proba))
demand_law = demand_law.freeze()
demand_law.pmf([0, 3]) # Probality Mass Function
demand_law.rvs(10) # Random Variables generation
invsys.perturb_laws = [demand_law] # a list, to support several perturbations
def admissible_orders(x):
'interval of allowed orders U(x_k)'
U1 = (0, 10)
return (U1, ) # tuple, to support several controls
# Attach it to the system description.
invsys.control_box = admissible_orders
### Cost description g = r(x) + c.u
(h, p, c) = 0.5, 3, 1
penal = (P_grid/power_max)**2
"""
P_prod = searev_power(Speed)
P_grid = P_prod - P_sto
penal = (P_grid / power_max) ** 2
return penal
cost_label = "quadratic cost"
### Create the system description:
searev_sys = SysDescription((3, 1, 1), name="Searev + Storage")
searev_sys.dyn = dyn_searev_sto
searev_sys.control_box = admissible_controls
searev_sys.cost = cost_model
searev_sys.perturb_laws = [innov_law]
# searev_sys.print_summary()
### Create the DP solver:
dpsolv = DPSolver(searev_sys)
# discretize the state space
N_E = 31
N_S = 61
N_A = 61
S_min, S_max = -4 * 0.254, 4 * 0.254
A_min, A_max = -4 * 0.227, 4 * 0.227
x_grid = dpsolv.discretize_state(0, E_rated, N_E, S_min, S_max, N_S, A_min, A_max, N_A)
E_grid, S_grid, A_grid = x_grid
# discretize the perturbation
N_w = 9
