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Switching to tx_util piecewise validator #278

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May 5, 2022
Merged

Switching to tx_util piecewise validator #278

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256b82a
switching to tx_util piecewise validator
bknueven May 3, 2022
0be8e9d
updating tiny_uc_8 baseline
bknueven May 4, 2022
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136 changes: 19 additions & 117 deletions egret/model_library/unit_commitment/params.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1007,13 +1007,6 @@ def _check_curve(m, g, curve, curve_type):
curve_t = curve
_t = None

tx_utils.validate_and_clean_cost_curve(curve_t,
curve_type=curve_type,
p_min=value(m.MinimumPowerOutput[g, t]),
p_max=value(m.MaximumPowerOutput[g, t]),
gen_name=g,
t=_t)

# if no curve_type+'_type' is specified, we assume piecewise (for backwards
# compatibility with no 'fuel_curve_type')
if curve_type + '_type' in curve and \
Expand All @@ -1040,24 +1033,12 @@ def validate_cost_rule(m, g):
if cost is not None and fuel is not None:
logger.warning("WARNING: ignoring provided p_cost and using fuel cost data from p_fuel for generator {}".format(g))

## look at p_cost through time
if fuel is None:
_check_curve(m, g, cost, 'cost_curve')
else:
if fuel_cost is None:
raise Exception("Found fuel_curve but not fuel_cost for generator {}".format(g))
_check_curve(m, g, fuel, 'fuel_curve')
for i, t in enumerate(m.TimePeriods):
if fuel_cost is dict:
if fuel_cost['data_type'] != 'time_series':
raise Exception("fuel_cost must be either numeric or time_series")
fuel_cost_t = fuel_cost['values'][i]
else:
fuel_cost_t = fuel_cost
if fuel_cost_t < 0:
raise Exception("fuel_cost must be non-negative, found negative fuel_cost for generator {}".format(g))
if fuel_cost_t == fuel_cost:
break
return True

model.ValidateGeneratorCost = BuildCheck(model.ThermalGenerators, rule=validate_cost_rule)
Expand Down Expand Up @@ -1095,103 +1076,23 @@ def validate_cost_rule(m, g):
_minimum_production_cost = {}
_minimum_fuel_consumption = {}

def _piecewise_adjustment_helper(m, g, p_min, p_max, curve, curve_type, t):

def _eliminate_piecewise_duplicates(input_func):
if len(input_func) <= 1:
return input_func
new = [input_func[0]]
for (o1, c1), (o2, c2) in zip(input_func, input_func[1:]):
if not math.isclose(o1,o2) and not math.isclose(c1,c2):
new.append((o2,c2))
return new

def _much_less_than(v1, v2):
return v1 < v2 and not math.isclose(v1,v2)

def _piecewise_adjustment_helper(m, p_min, p_max, input_func):

minimum_val = 0.
new_points = []
new_vals = []

input_func = _eliminate_piecewise_duplicates(input_func)
cleaned_values = tx_utils.validate_and_clean_cost_curve(curve,
curve_type=curve_type,
p_min=p_min,
p_max=p_max,
gen_name=g,
t=t)

set_p_min = False
p_min, p_min_cost = cleaned_values[0]
new_points = [0.,]
new_vals = [0.,]
for pnt, val in cleaned_values[1:]:
new_points.append(pnt-p_min)
new_vals.append(val-p_min_cost)

# NOTE: this implicitly inserts a (0.,0.)
# into every cost array
prior_output, prior_cost = 0., 0.

for output, cost in input_func:
## catch this case
if math.isclose(output, p_min) and math.isclose(output, p_max):
new_points.append(0.)
new_vals.append(0.)
minimum_val = cost
break

## output < p_min
elif _much_less_than(output, p_min):
pass

## p_min == output
elif math.isclose(output, p_min):
assert set_p_min is False
new_points.append(0.)
new_vals.append(0.)
minimum_val = cost
set_p_min = True

## p_min < output
elif _much_less_than(p_min, output) and _much_less_than(output, p_max):
if not set_p_min:
new_points.append(0.)
new_vals.append(0.)

price = ((cost-prior_cost)/(output-prior_output))
minimum_val = (p_min - prior_output) * price + prior_cost

new_points.append( output - p_min )
new_vals.append( (output - p_min) * price )

set_p_min = True
else:
new_points.append( output - p_min )
new_vals.append( cost - minimum_val )

elif math.isclose(output, p_max) or _much_less_than(p_max, output):
if not set_p_min:
new_points.append(0.)
new_vals.append(0.)

price = ((cost-prior_cost)/(output-prior_output))
minimum_val = (p_min - prior_output) * price + prior_cost

new_points.append( p_max - p_min )

if math.isclose(output, p_max):
new_vals.append( cost - minimum_val )
else:
new_vals.append( (p_max - p_min) * price )
set_p_min = True

else:
new_points.append( p_max - p_min )
if math.isclose(output, p_max):
new_vals.append( cost - minimum_val )
else:
price = ((cost-prior_cost)/(output-prior_output))
new_vals.append( (p_max - prior_output) * price + prior_cost - minimum_val )

break

else:
raise Exception("Unexpected case in _piecewise_adjustment_helper, "
"p_min={}, p_max={}, output={}".format(p_min, p_max, output))

prior_output, prior_cost = output, cost

return new_points, new_vals, minimum_val
return new_points, new_vals, p_min_cost

def _polynomial_to_piecewise_helper(m, p_min, p_max, input_func):
segment_max = value(m.NumGeneratorCostCurvePieces)
Expand Down Expand Up @@ -1227,12 +1128,13 @@ def _polynomial_to_piecewise_helper(m, p_min, p_max, input_func):

return new_points, new_vals, minimum_val

def _piecewise_helper(m, p_min, p_max, curve, curve_type):
def _piecewise_helper(m, g, p_min, p_max, curve, curve_type, t):
if curve_type not in curve or \
curve[curve_type] == 'piecewise':
return _piecewise_adjustment_helper(m, p_min, p_max, curve['values'])
return _piecewise_adjustment_helper(m, g, p_min, p_max, curve, curve['data_type'], t)
else:
assert curve[curve_type] == 'polynomial'
tx_utils.validate_and_clean_cost_curve(curve, curve['data_type'], p_min, p_max, g, t)
return _polynomial_to_piecewise_helper(m, p_min, p_max, curve['values'])


Expand Down Expand Up @@ -1289,7 +1191,7 @@ def power_generation_piecewise_points_rule(m, g):
m.PowerGenerationPiecewiseCostValues[g,t] = curve['cost_values']
continue

points, values, minimum_val = _piecewise_helper(m, p_min, p_max, fuel_curve, 'fuel_curve_type')
points, values, minimum_val = _piecewise_helper(m, g, p_min, p_max, fuel_curve, 'fuel_curve_type',t)

curve = { 'points' : points }

Expand Down Expand Up @@ -1346,7 +1248,7 @@ def power_generation_piecewise_points_rule(m, g):
values = [0., 0.]
min_production = nlc
else:
points, values, minimum_val = _piecewise_helper(m, p_min, p_max, cost_curve, 'cost_curve_type')
points, values, minimum_val = _piecewise_helper(m, g, p_min, p_max, cost_curve, 'cost_curve_type', t)
min_production = minimum_val + nlc

curve = {'points':points, 'cost_values':values, 'min_production':min_production}
Expand Down
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