Network Charges
energypylinear has the ability to optimize for a network charge.
A network charge is a tariff applied to a site based on the power consumed in certain intervals. It's often set to incentive reductions in demand during peak periods.
No Network Charge
Asset, Interval Data and Objective Function
First we will setup a battery with no network charge, and optimize it for electricity prices:
import energypylinear as epl
electricity_prices = [50, 100, 150]
asset = epl.Battery(electricity_prices=electricity_prices, efficiency=0.9)
bau = asset.optimize(
{
"terms": [
{
"asset_type": "site",
"variable": "import_power_mwh",
"interval_data": "electricity_prices",
},
{
"asset_type": "site",
"variable": "export_power_mwh",
"interval_data": "electricity_prices",
"coefficient": -1,
},
]
},
verbose=5
)
Results
We can then calculate the net import and battery charge:
results = bau.results
results["battery-net_charge_mwh"] = (
results["battery-electric_charge_mwh"] - results["battery-electric_discharge_mwh"]
)
results["site-net_import_mwh"] = (
results["site-import_power_mwh"] - results["site-export_power_mwh"]
)
print(
bau.results[
[
"site-electricity_prices",
"site-net_import_mwh",
"battery-net_charge_mwh",
"battery-electric_final_charge_mwh",
]
]
)
As expected, our battery charges during the first two intervals when electricity prices are low, and discharges during the third interval when prices are high:
site-electricity_prices site-net_import_mwh battery-net_charge_mwh battery-electric_final_charge_mwh
0 50.0 2.000000 2.000000 1.8
1 100.0 0.222222 0.222222 2.0
2 150.0 -2.000000 -2.000000 0.0
With Network Charge
Asset and Interval Data
By default, energypylinear uses interval data like electricity_prices or electricity_carbon_intensities. This interval data is supplied when initializing an asset or site.
For network charges, we will make use of the ability to supply custom interval data. Any extra keyword arguments supplied to an asset or site will be attempted to be parsed as interval data.
Below we setup a battery with both electricity prices and a network charge:
import energypylinear as epl
assert electricity_prices == [50, 100, 150]
network_charge = [0, 100, 0]
asset = epl.Battery(electricity_prices=electricity_prices, network_charge=network_charge)
Objective Function
By default, energypylinear has two built-in objective functions - price and carbon.
In order to optimize for a network charge, we need to supply a custom objective function. This function will be passed to the optimize method of an asset or site.
Below we optimize our battery with a custom objective function:
network_charge = asset.optimize(
{
"terms": [
{
"asset_type": "site",
"variable": "import_power_mwh",
"interval_data": "electricity_prices",
},
{
"asset_type": "site",
"variable": "export_power_mwh",
"interval_data": "electricity_prices",
"coefficient": -1,
},
{
"asset_type": "site",
"variable": "import_power_mwh",
"interval_data": "network_charge",
"coefficient": 1,
},
]
},
verbose=3
)
Results
results = network_charge.results
results["battery-net_charge_mwh"] = (
results["battery-electric_charge_mwh"] - results["battery-electric_discharge_mwh"]
)
results["site-net_import_mwh"] = (
results["site-import_power_mwh"] - results["site-export_power_mwh"]
)
print(
network_charge.results[
[
"site-electricity_prices",
"site-network_charge",
"site-net_import_mwh",
"battery-net_charge_mwh",
"battery-electric_final_charge_mwh",
]
]
)
We now see that our battery has not charged during the second interval, where we have a high site network charge: