def optimize_irrigated_area(self, zone) -> Dict:
"""Apply Linear Programming to naively optimize irrigated area.
Occurs at start of season.
Parameters
----------
* zone : FarmZone object, representing a farm or a farming zone.
"""
calc = []
areas = []
constraints = []
zone_ws = zone.water_sources
total_avail_water = zone.avail_allocation
field_areas = {}
for f in zone.fields:
area_to_consider = f.total_area_ha
did = f"{f.name}__".replace(" ", "_")
naive_crop_income = f.crop.estimate_income_per_ha()
naive_req_water = f.crop.water_use_ML_per_ha
app_cost_per_ML = self.ML_water_application_cost(zone, f, naive_req_water)
pos_field_area = [w.allocation / naive_req_water
for ws_name, w in zone_ws.items()
]
pos_field_area = min(sum(pos_field_area), area_to_consider)
field_areas[f.name] = {
ws_name: Variable(f"{did}{ws_name}",
lb=0,
ub=min(w.allocation / naive_req_water, area_to_consider))
for ws_name, w in zone_ws.items()
}
# total_pump_cost = sum([ws.pump.maintenance_cost(year_step) for ws in zone_ws])
profits = [field_areas[f.name][ws_name] *
(naive_crop_income - app_cost_per_ML[ws_name])
for ws_name in zone_ws
]
calc += profits
curr_field_areas = list(field_areas[f.name].values())
areas += curr_field_areas
# Total irrigated area cannot be greater than field area
# or area possible with available water
constraints += [
Constraint(sum(curr_field_areas), lb=0.0, ub=pos_field_area)
]
# End for
# for ws_name in zone_ws:
# total_f_ws = 0
# for f in zone.fields:
# total_f_ws += field_areas[f.name][ws_name]
# # End for
# # End for
# constraints += [Constraint(total_f_ws,
# lb=0.0,
# ub=zone.total_area_ha)]
constraints += [Constraint(sum(areas),
lb=0.0,
ub=zone.total_area_ha)]
# Generate appropriate OptLang model
model = Model.clone(self.opt_model)
model.objective = Objective(sum(calc), direction='max')
model.add(constraints)
model.optimize()
if model.status != 'optimal':
raise RuntimeError("Could not optimize!")
return model.primal_values
def optimize_irrigation(self, zone, dt: object) -> tuple:
"""Apply Linear Programming to optimize irrigation water use.
Results can be used to represent percentage mix
e.g. if the field area is 100 ha, and the optimal area to be
irrigated by a water source is
`SW: 70 ha
GW: 30 ha`
and the required amount is 20mm
`SW: 70 / 100 = 0.7 (irrigated area / total area, 70%)
GW: 30 / 100 = 0.3 (30%)`
Then the per hectare amount to be applied from each
water source is calculated as:
`SW = 20mm * 0.7
= 14mm
GW = 20mm * 0.3
= 6mm`
Parameters
----------
* zone : FarmZone
* dt : datetime object, current datetime
Returns
---------
* Tuple : OrderedDict[str, float] : keys based on field and water source names
values are hectare area
Float : $/ML cost of applying water
"""
model = self.opt_model
areas = []
profit = []
app_cost = OrderedDict()
constraints = []
zone_ws = zone.water_sources
total_irrigated_area = sum(map(lambda f: f.irrigated_area
if f.irrigated_area is not None
else 0.0, zone.fields))
field_area = {}
possible_area = {}
for f in zone.fields:
f_name = f.name
did = f"{f_name}__".replace(" ", "_")
if f.irrigation.name == 'dryland':
areas += [Variable(f"{did}{ws_name}", lb=0, ub=0)
for ws_name in zone_ws]
continue
# End if
# Disable this for now - estimated income includes variable costs
# Will always incur maintenance costs and crop costs
# total_pump_cost = sum([ws.pump.maintenance_cost(dt.year) for ws in zone_ws])
# total_irrig_cost = f.irrigation.maintenance_cost(dt.year)
# maintenance_cost = (total_pump_cost + total_irrig_cost)
# estimated gross income - variable costs per ha
crop_income_per_ha = f.crop.estimate_income_per_ha()
req_water_ML_ha = f.calc_required_water(dt) / ML_to_mm
if req_water_ML_ha == 0.0:
field_area[f_name] = {
ws_name: Variable(f"{did}{ws_name}",
lb=0.0,
ub=0.0)
for ws_name in zone_ws
}
else:
max_ws_area = zone.possible_area_by_allocation(f)
field_area[f_name] = {
ws_name: Variable(f"{did}{ws_name}",
lb=0,
ub=max_ws_area[ws_name])
for ws_name in zone_ws
}
# End if
# Costs to pump needed water volume from each water source
app_cost_per_ML = self.ML_water_application_cost(zone, f, req_water_ML_ha)
app_cost.update({
f"{did}{k}": v
for k, v in app_cost_per_ML.items()
})
profit += [
(crop_income_per_ha
- (app_cost_per_ML[ws_name] * req_water_ML_ha)
) * field_area[f_name][ws_name]
for ws_name in zone_ws
]
# End for
# Total irrigation area cannot be more than available area
constraints += [Constraint(sum(areas),
lb=0.0,
ub=min(total_irrigated_area, zone.total_area_ha))
]
# 0 <= field1*sw + field2*sw + field_n*sw <= possible area to be irrigated by sw
for ws_name, w in zone_ws.items():
alloc = w.allocation
pos_area = zone.possible_irrigation_area(alloc)
f_ws_var = []
for f in zone.fields:
f_ws_var += [field_area[f.name][ws_name]]
# End for
constraints += [Constraint(sum(f_ws_var),
lb=0.0,
ub=pos_area)]
# End for
# Generate appropriate OptLang model
model = Model.clone(self.opt_model)
model.objective = Objective(sum(profit), direction='max')
model.add(constraints)
model.optimize()
return model.primal_values, app_cost
