Electric Vehicles

A natural response when you get access to something someone else built is to wonder - does this work correctly?

This section will give you confidence in the implementation of the EV asset.

Fully Constrained EV Charging

import energypylinear as epl

asset = epl.EVs(
    chargers_power_mw=[100, 100],
    charge_events_capacity_mwh=[50, 100, 30],
    charger_turndown=0.0,
    charge_event_efficiency=1.0,
    electricity_prices=[-100, 50, 30, 10, 40],
    charge_events=[
        [1, 0, 0, 0, 0],
        [0, 1, 0, 0, 0],
        [0, 0, 1, 0, 0],
    ]
)
simulation = asset.optimize()
asset.plot(simulation, path="./docs/docs/static/ev-validation-1.png")

The third charger is the spill charger.

Expanding a Charge Event Window

Let's expand out the charge event window to the last three intervals for the last charge event:

import energypylinear as epl

asset = epl.EVs(
    chargers_power_mw=[100, 100],
    charge_events_capacity_mwh=[50, 100, 30],
    charger_turndown=0.0,
    charge_event_efficiency=1.0,
    electricity_prices=[-100, 50, 300, 10, 40],
    charge_events=[
        [1, 0, 0, 0, 0],
        [0, 1, 0, 0, 0],
        [0, 0, 1, 1, 1],
    ]
)
simulation = asset.optimize()
asset.plot(simulation, path="./docs/docs/static/ev-validation-2.png")

Now we see that the charge has happened in interval 3, this is because electricity prices are lowest in this interval.

Overlapping Charge Events

When charge events overlap at low prices, both (but only two) chargers are used:

import energypylinear as epl

asset = epl.EVs(
    chargers_power_mw=[100, 100],
    charge_events_capacity_mwh=[50, 100, 30],
    charger_turndown=0.0,
    charge_event_efficiency=1.0,
    electricity_prices=[-100, 50, 300, 10, 40],
    charge_events=[
        [1, 0, 0, 1, 0],
        [0, 1, 1, 1, 1],
        [0, 0, 1, 1, 1],
    ]
)
simulation = asset.optimize()
asset.plot(simulation, path="./docs/docs/static/ev-validation-3.png")

Adding V2G

import energypylinear as epl

asset = epl.EVs(
    chargers_power_mw=[100, 100],
    charge_events_capacity_mwh=[50, 100, 30],
    charger_turndown=0.0,
    charge_event_efficiency=1.0,
    electricity_prices=[-100, 50, 300, 10, 40],
    charge_events=[
        [1, 0, 0, 0, 0],
        [0, 1, 1, 1, 1],
        [0, 0, 1, 1, 1],
    ],
)
simulation = asset.optimize(
    flags=epl.Flags(allow_evs_discharge=True)
)
asset.plot(simulation, path="./docs/docs/static/ev-validation-4.png")

The key takeaway here is that we discharge during interval 2. All our charge events still end up at the correct state of charge at the end of the program.

Spill Chargers

import energypylinear as epl

asset = epl.EVs(
    chargers_power_mw=[100, 100],
    charge_events_capacity_mwh=[50, 100, 30, 500],
    charger_turndown=0.0,
    charge_event_efficiency=1.0,
    electricity_prices=[-100, 50, 300, 10, 40],
    charge_events=[
        [1, 0, 0, 0, 0],
        [0, 1, 1, 1, 1],
        [0, 0, 1, 1, 1],
        [1, 0, 0, 0, 0],
    ],
)
simulation = asset.optimize(
    flags=epl.Flags(allow_evs_discharge=True)
)
asset.plot(simulation, path="./docs/docs/static/ev-validation-5.png")

Key takeaway here is the use of the spill charger - we have a 500 MWh charge event, but only 200 MWh of capacity. We meet the remaining demand from a spill charger.

This allows the linear program to be feasible, while communicating directly which intervals or charge events are causing the mismatch between charge event demand and spill charger capacity.