def schedule_charging_station(
sensor: Sensor,
start: datetime,
end: datetime,
resolution: timedelta,
soc_at_start: float,
soc_targets: pd.Series,
soc_min: Optional[float] = None,
soc_max: Optional[float] = None,
roundtrip_efficiency: Optional[float] = None,
prefer_charging_sooner: bool = True,
price_sensor: Optional[Sensor] = None,
round_to_decimals: Optional[int] = 6,
) -> Union[pd.Series, None]:
"""Schedule a charging station asset based directly on the latest beliefs regarding market prices within the specified time
window.
For the resulting consumption schedule, consumption is defined as positive values.
Todo: handle uni-directional charging by setting the "min" or "derivative min" constraint to 0
"""
# Check for required Sensor attributes
sensor.check_required_attributes([("capacity_in_mw", (float, int))])
# Check for round-trip efficiency
if roundtrip_efficiency is None:
# Get default from sensor, or use 100% otherwise
roundtrip_efficiency = sensor.get_attribute("roundtrip_efficiency", 1)
if roundtrip_efficiency <= 0 or roundtrip_efficiency > 1:
raise ValueError(
"roundtrip_efficiency expected within the interval (0, 1]")
# Check for min and max SOC, or get default from sensor
if soc_min is None:
# Can't drain the EV battery by more than it contains
soc_min = sensor.get_attribute("min_soc_in_mwh", 0)
if soc_max is None:
# Lacking information about the battery's nominal capacity, we use the highest target value as the maximum state of charge
soc_max = sensor.get_attribute("max_soc_in_mwh",
max(soc_targets.values))
# Check for known prices or price forecasts, trimming planning window accordingly
prices, (start, end) = get_prices(
(start, end),
resolution,
price_sensor=price_sensor,
sensor=sensor,
allow_trimmed_query_window=True,
)
# soc targets are at the end of each time slot, while prices are indexed by the start of each time slot
soc_targets = soc_targets.tz_convert("UTC")
start = pd.Timestamp(start).tz_convert("UTC")
end = pd.Timestamp(end).tz_convert("UTC")
soc_targets = soc_targets[start + resolution:end]
# Add tiny price slope to prefer charging now rather than later, and discharging later rather than now.
# We penalise the future with at most 1 per thousand times the price spread.
if prefer_charging_sooner:
prices = add_tiny_price_slope(prices, "event_value")
# Set up commitments to optimise for
commitment_quantities = [initialize_series(0, start, end, resolution)]
# Todo: convert to EUR/(deviation of commitment, which is in MW)
commitment_upwards_deviation_price = [
prices.loc[start:end - resolution]["event_value"]
]
commitment_downwards_deviation_price = [
prices.loc[start:end - resolution]["event_value"]
]
# Set up device constraints (only one device for this EMS)
columns = [
"equals",
"max",
"min",
"derivative equals",
"derivative max",
"derivative min",
]
device_constraints = [initialize_df(columns, start, end, resolution)]
device_constraints[0]["equals"] = soc_targets.shift(
-1, freq=resolution
).values * (timedelta(hours=1) / resolution) - soc_at_start * (
timedelta(hours=1) / resolution
) # shift "equals" constraint for target SOC by one resolution (the target defines a state at a certain time,
# while the "equals" constraint defines what the total stock should be at the end of a time slot,
# where the time slot is indexed by its starting time)
device_constraints[0]["min"] = (soc_min - soc_at_start) * (
timedelta(hours=1) / resolution)
device_constraints[0]["max"] = (soc_max - soc_at_start) * (
timedelta(hours=1) / resolution)
if sensor.get_attribute("is_strictly_non_positive"):
device_constraints[0]["derivative min"] = 0
else:
device_constraints[0]["derivative min"] = (
sensor.get_attribute("capacity_in_mw") * -1)
if sensor.get_attribute("is_strictly_non_negative"):
device_constraints[0]["derivative max"] = 0
else:
device_constraints[0]["derivative max"] = sensor.get_attribute(
"capacity_in_mw")
# Apply round-trip efficiency evenly to charging and discharging
device_constraints[0][
"derivative down efficiency"] = roundtrip_efficiency**0.5
device_constraints[0][
"derivative up efficiency"] = roundtrip_efficiency**0.5
# Set up EMS constraints (no additional constraints)
columns = ["derivative max", "derivative min"]
ems_constraints = initialize_df(columns, start, end, resolution)
ems_schedule, expected_costs, scheduler_results = device_scheduler(
device_constraints,
ems_constraints,
commitment_quantities,
commitment_downwards_deviation_price,
commitment_upwards_deviation_price,
)
if scheduler_results.solver.termination_condition == "infeasible":
# Fallback policy if the problem was unsolvable
charging_station_schedule = fallback_charging_policy(
sensor, device_constraints[0], start, end, resolution)
else:
charging_station_schedule = ems_schedule[0]
# Round schedule
if round_to_decimals:
charging_station_schedule = charging_station_schedule.round(
round_to_decimals)
return charging_station_schedule
def schedule_charging_station(
asset: Asset,
market: Market,
start: datetime,
end: datetime,
resolution: timedelta,
soc_at_start: float,
soc_targets: Series,
prefer_charging_sooner: bool = True,
) -> Union[Series, None]:
"""Schedule a charging station asset based directly on the latest beliefs regarding market prices within the specified time
window.
For the resulting consumption schedule, consumption is defined as positive values.
Todo: handle uni-directional charging by setting the "min" or "derivative min" constraint to 0
"""
# Check for known prices or price forecasts, trimming planning window accordingly
prices, (start, end) = get_prices(market, (start, end),
resolution,
allow_trimmed_query_window=True)
# soc targets are at the end of each time slot, while prices are indexed by the start of each time slot
soc_targets = soc_targets[start + resolution:end]
# Add tiny price slope to prefer charging now rather than later, and discharging later rather than now.
# We penalise the future with at most 1 per thousand times the price spread.
if prefer_charging_sooner:
prices = add_tiny_price_slope(prices, "event_value")
# Set up commitments to optimise for
commitment_quantities = [initialize_series(0, start, end, resolution)]
# Todo: convert to EUR/(deviation of commitment, which is in MW)
commitment_upwards_deviation_price = [
prices.loc[start:end - resolution]["event_value"]
]
commitment_downwards_deviation_price = [
prices.loc[start:end - resolution]["event_value"]
]
# Set up device constraints (only one device for this EMS)
columns = [
"equals",
"max",
"min",
"derivative equals",
"derivative max",
"derivative min",
]
device_constraints = [initialize_df(columns, start, end, resolution)]
device_constraints[0]["equals"] = soc_targets.shift(
-1, freq=resolution
).values * (timedelta(hours=1) / resolution) - soc_at_start * (
timedelta(hours=1) / resolution
) # shift "equals" constraint for target SOC by one resolution (the target defines a state at a certain time,
# while the "equals" constraint defines what the total stock should be at the end of a time slot,
# where the time slot is indexed by its starting time)
device_constraints[0]["min"] = -soc_at_start * (
timedelta(hours=1) / resolution
) # Can't drain the EV battery by more than it contains
device_constraints[0]["max"] = max(
soc_targets.values
) * (timedelta(hours=1) / resolution) - soc_at_start * (
timedelta(hours=1) / resolution
) # Lacking information about the battery's nominal capacity, we use the highest target value as the maximum state of charge
if asset.is_pure_consumer:
device_constraints[0]["derivative min"] = 0
else:
device_constraints[0]["derivative min"] = asset.capacity_in_mw * -1
if asset.is_pure_producer:
device_constraints[0]["derivative max"] = 0
else:
device_constraints[0]["derivative max"] = asset.capacity_in_mw
# Set up EMS constraints (no additional constraints)
columns = ["derivative max", "derivative min"]
ems_constraints = initialize_df(columns, start, end, resolution)
ems_schedule, expected_costs = device_scheduler(
device_constraints,
ems_constraints,
commitment_quantities,
commitment_downwards_deviation_price,
commitment_upwards_deviation_price,
)
charging_station_schedule = ems_schedule[0]
return charging_station_schedule
def device_scheduler( # noqa C901
device_constraints: List[pd.DataFrame],
ems_constraints: pd.DataFrame,
commitment_quantities: List[pd.Series],
commitment_downwards_deviation_price: Union[List[pd.Series], List[float]],
commitment_upwards_deviation_price: Union[List[pd.Series], List[float]],
) -> Tuple[List[pd.Series], float, SolverResults]:
"""This generic device scheduler is able to handle an EMS with multiple devices,
with various types of constraints on the EMS level and on the device level,
and with multiple market commitments on the EMS level.
A typical example is a house with many devices.
The commitments are assumed to be with regard to the flow of energy to the device (positive for consumption,
negative for production). The solver minimises the costs of deviating from the commitments.
Device constraints are on a device level. Handled constraints (listed by column name):
max: maximum stock assuming an initial stock of zero (e.g. in MWh or boxes)
min: minimum stock assuming an initial stock of zero
equal: exact amount of stock (we do this by clamping min and max)
derivative max: maximum flow (e.g. in MW or boxes/h)
derivative min: minimum flow
derivative equals: exact amount of flow (we do this by clamping derivative min and derivative max)
derivative down efficiency: ratio of downwards flows (flow into EMS : flow out of device)
derivative up efficiency: ratio of upwards flows (flow into device : flow out of EMS)
EMS constraints are on an EMS level. Handled constraints (listed by column name):
derivative max: maximum flow
derivative min: minimum flow
Commitments are on an EMS level. Parameter explanations:
commitment_quantities: amounts of flow specified in commitments (both previously ordered and newly requested)
- e.g. in MW or boxes/h
commitment_downwards_deviation_price: penalty for downwards deviations of the flow
- e.g. in EUR/MW or EUR/(boxes/h)
- either a single value (same value for each flow value) or a Series (different value for each flow value)
commitment_upwards_deviation_price: penalty for upwards deviations of the flow
All Series and DataFrames should have the same resolution.
For now, we pass in the various constraints and prices as separate variables, from which we make a MultiIndex
DataFrame. Later we could pass in a MultiIndex DataFrame directly.
"""
# If the EMS has no devices, don't bother
if len(device_constraints) == 0:
return [], 0, SolverResults()
# Check if commitments have the same time window and resolution as the constraints
start = device_constraints[0].index.to_pydatetime()[0]
resolution = pd.to_timedelta(device_constraints[0].index.freq)
end = device_constraints[0].index.to_pydatetime()[-1] + resolution
if len(commitment_quantities) != 0:
start_c = commitment_quantities[0].index.to_pydatetime()[0]
resolution_c = pd.to_timedelta(commitment_quantities[0].index.freq)
end_c = commitment_quantities[0].index.to_pydatetime()[-1] + resolution
if not (start_c == start and end_c == end):
raise Exception(
"Not implemented for different time windows.\n(%s,%s)\n(%s,%s)"
% (start, end, start_c, end_c))
if resolution_c != resolution:
raise Exception(
"Not implemented for different resolutions.\n%s\n%s" %
(resolution, resolution_c))
# Turn prices per commitment into prices per commitment flow
if len(commitment_downwards_deviation_price) != 0:
if all(
isinstance(price, float)
for price in commitment_downwards_deviation_price):
commitment_downwards_deviation_price = [
initialize_series(price, start, end, resolution)
for price in commitment_downwards_deviation_price
]
if len(commitment_upwards_deviation_price) != 0:
if all(
isinstance(price, float)
for price in commitment_upwards_deviation_price):
commitment_upwards_deviation_price = [
initialize_series(price, start, end, resolution)
for price in commitment_upwards_deviation_price
]
model = ConcreteModel()
# Add indices for devices (d), datetimes (j) and commitments (c)
model.d = RangeSet(0, len(device_constraints) - 1, doc="Set of devices")
model.j = RangeSet(0,
len(device_constraints[0].index.to_pydatetime()) - 1,
doc="Set of datetimes")
model.c = RangeSet(0,
len(commitment_quantities) - 1,
doc="Set of commitments")
# Add parameters
def price_down_select(m, c, j):
return commitment_downwards_deviation_price[c].iloc[j]
def price_up_select(m, c, j):
return commitment_upwards_deviation_price[c].iloc[j]
def commitment_quantity_select(m, c, j):
return commitment_quantities[c].iloc[j]
def device_max_select(m, d, j):
max_v = device_constraints[d]["max"].iloc[j]
equal_v = device_constraints[d]["equals"].iloc[j]
if np.isnan(max_v) and np.isnan(equal_v):
return infinity
else:
return np.nanmin([max_v, equal_v])
def device_min_select(m, d, j):
min_v = device_constraints[d]["min"].iloc[j]
equal_v = device_constraints[d]["equals"].iloc[j]
if np.isnan(min_v) and np.isnan(equal_v):
return -infinity
else:
return np.nanmax([min_v, equal_v])
def device_derivative_max_select(m, d, j):
max_v = device_constraints[d]["derivative max"].iloc[j]
equal_v = device_constraints[d]["derivative equals"].iloc[j]
if np.isnan(max_v) and np.isnan(equal_v):
return infinity
else:
return np.nanmin([max_v, equal_v])
def device_derivative_min_select(m, d, j):
min_v = device_constraints[d]["derivative min"].iloc[j]
equal_v = device_constraints[d]["derivative equals"].iloc[j]
if np.isnan(min_v) and np.isnan(equal_v):
return -infinity
else:
return np.nanmax([min_v, equal_v])
def ems_derivative_max_select(m, j):
v = ems_constraints["derivative max"].iloc[j]
if np.isnan(v):
return infinity
else:
return v
def ems_derivative_min_select(m, j):
v = ems_constraints["derivative min"].iloc[j]
if np.isnan(v):
return -infinity
else:
return v
def device_derivative_down_efficiency(m, d, j):
try:
return device_constraints[d]["derivative down efficiency"].iloc[j]
except KeyError:
return 1
def device_derivative_up_efficiency(m, d, j):
try:
return device_constraints[d]["derivative up efficiency"].iloc[j]
except KeyError:
return 1
model.up_price = Param(model.c, model.j, initialize=price_up_select)
model.down_price = Param(model.c, model.j, initialize=price_down_select)
model.commitment_quantity = Param(model.c,
model.j,
initialize=commitment_quantity_select)
model.device_max = Param(model.d, model.j, initialize=device_max_select)
model.device_min = Param(model.d, model.j, initialize=device_min_select)
model.device_derivative_max = Param(
model.d, model.j, initialize=device_derivative_max_select)
model.device_derivative_min = Param(
model.d, model.j, initialize=device_derivative_min_select)
model.ems_derivative_max = Param(model.j,
initialize=ems_derivative_max_select)
model.ems_derivative_min = Param(model.j,
initialize=ems_derivative_min_select)
model.device_derivative_down_efficiency = Param(
model.d, model.j, initialize=device_derivative_down_efficiency)
model.device_derivative_up_efficiency = Param(
model.d, model.j, initialize=device_derivative_up_efficiency)
# Add variables
model.ems_power = Var(model.d, model.j, domain=Reals, initialize=0)
model.device_power_down = Var(model.d,
model.j,
domain=NonPositiveReals,
initialize=0)
model.device_power_up = Var(model.d,
model.j,
domain=NonNegativeReals,
initialize=0)
model.commitment_downwards_deviation = Var(model.c,
model.j,
domain=NonPositiveReals,
initialize=0)
model.commitment_upwards_deviation = Var(model.c,
model.j,
domain=NonNegativeReals,
initialize=0)
# Add constraints as a tuple of (lower bound, value, upper bound)
def device_bounds(m, d, j):
return (
m.device_min[d, j],
sum(m.device_power_down[d, k] + m.device_power_up[d, k]
for k in range(0, j + 1)),
m.device_max[d, j],
)
def device_derivative_bounds(m, d, j):
return (
m.device_derivative_min[d, j],
m.device_power_down[d, j] + m.device_power_up[d, j],
m.device_derivative_max[d, j],
)
def device_down_derivative_bounds(m, d, j):
return (
m.device_derivative_min[d, j],
m.device_power_down[d, j],
0,
)
def device_up_derivative_bounds(m, d, j):
return (
0,
m.device_power_up[d, j],
m.device_derivative_max[d, j],
)
def ems_derivative_bounds(m, j):
return m.ems_derivative_min[j], sum(
m.ems_power[:, j]), m.ems_derivative_max[j]
def ems_flow_commitment_equalities(m, j):
"""Couple EMS flows (sum over devices) to commitments."""
return (
0,
sum(m.commitment_quantity[:, j]) +
sum(m.commitment_downwards_deviation[:, j]) +
sum(m.commitment_upwards_deviation[:, j]) - sum(m.ems_power[:, j]),
0,
)
def device_derivative_equalities(m, d, j):
"""Couple device flows to EMS flows per device, applying efficiencies."""
return (
0,
m.device_power_up[d, j] / m.device_derivative_up_efficiency[d, j] +
m.device_power_down[d, j] *
m.device_derivative_down_efficiency[d, j] - m.ems_power[d, j],
0,
)
model.device_energy_bounds = Constraint(model.d,
model.j,
rule=device_bounds)
model.device_power_bounds = Constraint(model.d,
model.j,
rule=device_derivative_bounds)
model.device_power_down_bounds = Constraint(
model.d, model.j, rule=device_down_derivative_bounds)
model.device_power_up_bounds = Constraint(model.d,
model.j,
rule=device_up_derivative_bounds)
model.ems_power_bounds = Constraint(model.j, rule=ems_derivative_bounds)
model.ems_power_commitment_equalities = Constraint(
model.j, rule=ems_flow_commitment_equalities)
model.device_power_equalities = Constraint(
model.d, model.j, rule=device_derivative_equalities)
# Add objective
def cost_function(m):
costs = 0
for c in m.c:
for j in m.j:
costs += m.commitment_downwards_deviation[c,
j] * m.down_price[c,
j]
costs += m.commitment_upwards_deviation[c, j] * m.up_price[c,
j]
return costs
model.costs = Objective(rule=cost_function, sense=minimize)
# Solve
results = SolverFactory(
current_app.config.get("FLEXMEASURES_LP_SOLVER")).solve(model)
planned_costs = value(model.costs)
planned_power_per_device = []
for d in model.d:
planned_device_power = [
model.device_power_down[d, j].value +
model.device_power_up[d, j].value for j in model.j
]
planned_power_per_device.append(
pd.Series(
index=pd.date_range(start=start,
end=end,
freq=to_offset(resolution),
closed="left"),
data=planned_device_power,
))
# model.pprint()
# print(results.solver.termination_condition)
# print(planned_costs)
# model.display()
return planned_power_per_device, planned_costs, results