Markets

This example shows how to include electricity markets in a deterministic dispatch problem. Generators can sell excess power into markets for additional revenue.

Source: examples/example4.py

What it demonstrates

• Adding multiple EnergyMarket instances with different volume limits
• Providing market_prices in the scenario
• How the optimizer decides where to sell excess generation

The setup

Two generators with different costs, a fixed load, and three electricity markets:

• sdac: Day-ahead market (max 150 MW/step)
• sidc1: Intraday market 1 (max 100 MW/step)
• sidc2: Intraday market 2 (max 50 MW/step)

Each market has its own price timeseries. The optimizer will dispatch generators to meet the load and then sell any excess into the most profitable market.

Code

from datetime import timedelta

from odys.energy_system import EnergySystem
from odys.energy_system_models.assets.generator import PowerGenerator
from odys.energy_system_models.assets.load import Load, LoadType
from odys.energy_system_models.assets.portfolio import AssetPortfolio
from odys.energy_system_models.markets import EnergyMarket
from odys.energy_system_models.scenarios import Scenario

generator_1 = PowerGenerator(
    name="gen1",
    nominal_power=100.0,
    variable_cost=20.0,
    min_up_time=1,
    ramp_down=100,
)
generator_2 = PowerGenerator(
    name="gen2",
    nominal_power=150.0,
    variable_cost=100.0,
    min_up_time=4,
    min_power=30,
    startup_cost=0,
    ramp_up=140,
    ramp_down=100,
)

portfolio = AssetPortfolio()
portfolio.add_asset(generator_1)
portfolio.add_asset(generator_2)
portfolio.add_asset(Load(name="load", type=LoadType.Fixed))

energy_system = EnergySystem(
    portfolio=portfolio,
    markets=(
        EnergyMarket(name="sdac", max_trading_volume_per_step=150),
        EnergyMarket(name="sidc1", max_trading_volume_per_step=100),
        EnergyMarket(name="sidc2", max_trading_volume_per_step=50),
    ),
    scenarios=Scenario(
        available_capacity_profiles={
            "gen1": [100, 100, 100, 50, 50, 50, 50],
        },
        load_profiles={"load": [200, 75, 200, 50, 100, 120, 125]},
        market_prices={
            "sdac": [150, 150, 150, 150, 150, 150, 150],
            "sidc1": [200, 200, 200, 175, 100, 100, 100],
            "sidc2": [190, 150, 150, 175, 100, 100, 100],
        },
    ),
    timestep=timedelta(minutes=30),
    number_of_steps=7,
    power_unit="MW",
)

result = energy_system.optimize()

Reading the results

print(result.generators.power)     # how much each generator produces
print(result.markets.sell_volume)   # how much is sold to each market
print(result.to_dataframe)          # everything in one place

What to look for

• The optimizer sells excess generation to whichever market pays the most at each timestep.
• sidc1 has higher prices than sdac in the first few steps, so it gets filled first (up to its volume limit).
• Generators may produce more than the load requires when market revenue exceeds operating costs.