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simple-electricity-market-examples.py
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# -*- coding: utf-8 -*-
## Simple electricity market examples
#
# This Jupyter notebook is meant for teaching purposes. To use it, you need to install a Python environment with Jupyter notebooks, and the Python for Power System Analysis (PyPSA) library. See
#
# https://pypsa.readthedocs.io/en/latest/installation.html
#
# for tips on installation.
#
# It gradually builds up more and more complicated energy-only electricity markets in PyPSA, starting from a single bidding zone, going up to multiple bidding zones connected with transmission (NTCs) along with variable renewables and storage.
#
# Available as a Jupyter notebook at https://pypsa.readthedocs.io/en/latest/examples/simple-electricity-market-examples.ipynb.
### Preliminaries
#
# Here libraries are imported and data is defined.
import numpy as np
import pypsa
# marginal costs in EUR/MWh
marginal_costs = {"Wind": 0, "Hydro": 0, "Coal": 30, "Gas": 60, "Oil": 80}
# power plant capacities (nominal powers in MW) in each country (not necessarily realistic)
power_plant_p_nom = {
"South Africa": {"Coal": 35000, "Wind": 3000, "Gas": 8000, "Oil": 2000},
"Mozambique": {
"Hydro": 1200,
},
"Swaziland": {
"Hydro": 600,
},
}
# transmission capacities in MW (not necessarily realistic)
transmission = {
"South Africa": {"Mozambique": 500, "Swaziland": 250},
"Mozambique": {"Swaziland": 100},
}
# country electrical loads in MW (not necessarily realistic)
loads = {"South Africa": 42000, "Mozambique": 650, "Swaziland": 250}
### Single bidding zone with fixed load, one period
#
# In this example we consider a single market bidding zone, South Africa.
#
# The inelastic load has essentially infinite marginal utility (or higher than the marginal cost of any generator).
country = "South Africa"
network = pypsa.Network()
network.add("Bus", country)
for tech in power_plant_p_nom[country]:
network.add(
"Generator",
"{} {}".format(country, tech),
bus=country,
p_nom=power_plant_p_nom[country][tech],
marginal_cost=marginal_costs[tech],
)
network.add("Load", "{} load".format(country), bus=country, p_set=loads[country])
# Run optimisation to determine market dispatch
network.lopf()
# print the load active power (P) consumption
network.loads_t.p
# print the generator active power (P) dispatch
network.generators_t.p
# print the clearing price (corresponding to gas)
network.buses_t.marginal_price
### Two bidding zones connected by transmission, one period
#
# In this example we have bidirectional transmission capacity between two bidding zones. The power transfer is treated as controllable (like an A/NTC (Available/Net Transfer Capacity) or HVDC line). Note that in the physical grid, power flows passively according to the network impedances.
network = pypsa.Network()
countries = ["Mozambique", "South Africa"]
for country in countries:
network.add("Bus", country)
for tech in power_plant_p_nom[country]:
network.add(
"Generator",
"{} {}".format(country, tech),
bus=country,
p_nom=power_plant_p_nom[country][tech],
marginal_cost=marginal_costs[tech],
)
network.add("Load", "{} load".format(country), bus=country, p_set=loads[country])
# add transmission as controllable Link
if country not in transmission:
continue
for other_country in countries:
if other_country not in transmission[country]:
continue
# NB: Link is by default unidirectional, so have to set p_min_pu = -1
# to allow bidirectional (i.e. also negative) flow
network.add(
"Link",
"{} - {} link".format(country, other_country),
bus0=country,
bus1=other_country,
p_nom=transmission[country][other_country],
p_min_pu=-1,
)
network.lopf()
network.loads_t.p
network.generators_t.p
network.links_t.p0
# print the clearing price (corresponding to water in Mozambique and gas in SA)
network.buses_t.marginal_price
# link shadow prices
network.links_t.mu_lower
### Three bidding zones connected by transmission, one period
#
# In this example we have bidirectional transmission capacity between three bidding zones. The power transfer is treated as controllable (like an A/NTC (Available/Net Transfer Capacity) or HVDC line). Note that in the physical grid, power flows passively according to the network impedances.
network = pypsa.Network()
countries = ["Swaziland", "Mozambique", "South Africa"]
for country in countries:
network.add("Bus", country)
for tech in power_plant_p_nom[country]:
network.add(
"Generator",
"{} {}".format(country, tech),
bus=country,
p_nom=power_plant_p_nom[country][tech],
marginal_cost=marginal_costs[tech],
)
network.add("Load", "{} load".format(country), bus=country, p_set=loads[country])
# add transmission as controllable Link
if country not in transmission:
continue
for other_country in countries:
if other_country not in transmission[country]:
continue
# NB: Link is by default unidirectional, so have to set p_min_pu = -1
# to allow bidirectional (i.e. also negative) flow
network.add(
"Link",
"{} - {} link".format(country, other_country),
bus0=country,
bus1=other_country,
p_nom=transmission[country][other_country],
p_min_pu=-1,
)
network.lopf()
network.loads_t.p
network.generators_t.p
network.links_t.p0
# print the clearing price (corresponding to hydro in S and M, and gas in SA)
network.buses_t.marginal_price
# link shadow prices
network.links_t.mu_lower
### Single bidding zone with price-sensitive industrial load, one period
#
# In this example we consider a single market bidding zone, South Africa.
#
# Now there is a large industrial load with a marginal utility which is low enough to interact with the generation marginal cost.
country = "South Africa"
network = pypsa.Network()
network.add("Bus", country)
for tech in power_plant_p_nom[country]:
network.add(
"Generator",
"{} {}".format(country, tech),
bus=country,
p_nom=power_plant_p_nom[country][tech],
marginal_cost=marginal_costs[tech],
)
# standard high marginal utility consumers
network.add("Load", "{} load".format(country), bus=country, p_set=loads[country])
# add an industrial load as a dummy negative-dispatch generator with marginal utility of 70 EUR/MWh for 8000 MW
network.add(
"Generator",
"{} industrial load".format(country),
bus=country,
p_max_pu=0,
p_min_pu=-1,
p_nom=8000,
marginal_cost=70,
)
network.lopf()
network.loads_t.p
# NB only half of industrial load is served, because this maxes out
# Gas. Oil is too expensive with a marginal cost of 80 EUR/MWh
network.generators_t.p
network.buses_t.marginal_price
### Single bidding zone with fixed load, several periods
#
# In this example we consider a single market bidding zone, South Africa.
#
# We consider multiple time periods (labelled [0, 1, 2, 3]) to represent variable wind generation.
country = "South Africa"
network = pypsa.Network()
# snapshots labelled by [0, 1, 2, 3]
network.set_snapshots(range(4))
network.add("Bus", country)
# p_max_pu is variable for wind
for tech in power_plant_p_nom[country]:
network.add(
"Generator",
"{} {}".format(country, tech),
bus=country,
p_nom=power_plant_p_nom[country][tech],
marginal_cost=marginal_costs[tech],
p_max_pu=([0.3, 0.6, 0.4, 0.5] if tech == "Wind" else 1),
)
# load which varies over the snapshots
network.add(
"Load",
"{} load".format(country),
bus=country,
p_set=loads[country] + np.array([0, 1000, 3000, 4000]),
)
# specify that we consider all snapshots
network.lopf(network.snapshots)
network.loads_t.p
network.generators_t.p
network.buses_t.marginal_price
### Single bidding zone with fixed load and storage, several periods
#
# In this example we consider a single market bidding zone, South Africa.
#
# We consider multiple time periods (labelled [0, 1, 2, 3]) to represent variable wind generation. Storage is allowed to do price arbitrage to reduce oil consumption.
country = "South Africa"
network = pypsa.Network()
# snapshots labelled by [0, 1, 2, 3]
network.set_snapshots(range(4))
network.add("Bus", country)
# p_max_pu is variable for wind
for tech in power_plant_p_nom[country]:
network.add(
"Generator",
"{} {}".format(country, tech),
bus=country,
p_nom=power_plant_p_nom[country][tech],
marginal_cost=marginal_costs[tech],
p_max_pu=([0.3, 0.6, 0.4, 0.5] if tech == "Wind" else 1),
)
# load which varies over the snapshots
network.add(
"Load",
"{} load".format(country),
bus=country,
p_set=loads[country] + np.array([0, 1000, 3000, 4000]),
)
# storage unit to do price arbitrage
network.add(
"StorageUnit",
"{} pumped hydro".format(country),
bus=country,
p_nom=1000,
max_hours=6, # energy storage in terms of hours at full power
)
network.lopf(network.snapshots)
network.loads_t.p
network.generators_t.p
network.storage_units_t.p
network.storage_units_t.state_of_charge
network.buses_t.marginal_price