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Power sector

Key features

  • Co-optimisation of generation and capacity expansion including interconnections.
  • Myopic (year-by-year) optimisation. Each year is optimised independently, without assuming knowledge of future developments.
  • Brownfield modelling approach. The model builds on existing infrastructure, meaning capacity from previous years is retained and carried forward.

PyPSA-SPICE follows the component definitions from PyPSA components. The diagram below illustrates all components involved in energy flows at a single node in the power sector.

PyPSA-SPICE power sector energy flow

Power generators

All the listed components are defined as Generator in PyPSA.

Abbreviation Full Name
CSP Concentrated solar power plant
GEOT Geothermal power plant
HROR Hydro run-of-river
PHOT Solar PV
RTPV Rooftop PV
WTOF Offshore wind
WTON Onshore wind

All the listed components are defined as Link in PyPSA.

Abbreviation Full Name
BIOT Biomass power plant
CCGT Combined-cycle gas turbine power plant
CHP Combined heat and power plant
ELTZ Electrolyser (for hydrogen production)
NUCL Nuclear power plant
OCGT Open-cycle gas turbine power plant
OCHT Open-cycle hydrogen turbine power plant
OILT Oil turbine power plant power plant
SubC Subcritical coal-fired power plant
SupC Supercritical coal-fired power plant
WSTT Waste-to-energy power plant

Storage capacity

The following component is defined as StorageUnit in PyPSA.

Storages can be modelled with two approaches.

  1. Fixed energy-to-power ratio: In this case, the energy to power ratio for storage is predefined. You can use multiple storage type with different energy to power ratio. For example, BATS with E/P ratio of 4 and BATS with E/P ratio of 8 representing different energy to power ratio and the model will optimise the capacity of each of these technology. In this case, PyPSA type Storage_units can be used for modelling and defining the energy to power ratio in technologies.csv.
  2. Variable energy-to-power ratio: If you want the model to optimise the energy/power ratio of storage your have to model it using a combination of Links + Store component. This requires separate inputs like costs for capacity and energy component of the storage inputs.

Tip

In PyPSA components, StorageUnit is modelled as a storage asset with a fixed energy-to-power ratio defined by max_hours of the nominal power (you can also refer to PyPSA Components - StorageUnit for more information). Thus, in PyPSA-SPICE model builder, hydro dam HDAM is defined as a StorageUnit and it is given in storage capcaity only to represent nominal power-related params.

To model the storage energy separately from the power capacity, Store + 2 Links is a better combination. You can refer to Storage energy for more information. Technologies defined in the storage energy require storage capacity if the carrier is related to electricity (power).

Abbreviation Full Name
HDAM Hydro dam
BATS Utility-scale battery storage
HHBS Household battery storage
HPHS Hydro pumped storage

Storage energy

All the listed components are defined as Store in PyPSA.

Tip

In PyPSA components, Store is modelled as a storage asset with only energy storage. It can optimise energy capacity separately from the power capacity with a combination of Store + 2 Links. The links represent charging and discharging characteristics to control the power output. Marginal cost and efficiency of charging and discharging can be defined in each link.

In PyPSA-SPICE model builder, technologies that are defined as storage energy, they should also be included in Storage capacity to describe charging and discharging processes. The links are created automatically , and hence it's not required to add charging and discharging links inside Power links.

Detailed information and example can be found in PyPSA Components - Store and Replace StorageUnits with fundamental Links and Stores.

Abbreviation Full Name
CO2STOR CO2 storage
BATS Utility-scale battery storage
HHBS Household battery storage
HPHS Hydro pumped storage

Carriers

Abbreviation Full Name
Bio Biomass
Bit Bituminous or brown coal
CO2 Carbon dioxide (in the atmosphere)
Co2stor Captured carbon dioxide
Electricity Electricity
Gas Domestic natural gas
Gas-imp Imported natural gas
High_Heat High-temperature heat (> 350°C)
Hrdc Anthracite or hard coal
Hyd Hydrogen
Lig Lignite
Lng Liquefied natural gas
Low_Heat Low-/Medium-temperature heat (< 350°C)
Oil Oil
Uranium Uranium
Waste Waste

Buses

Abbreviation Full Name
ATMP Atmosphere
BATSN Lithium battery storage
BION Biomass
BITN Bituminous
CO2STORN CO2 storage
GASN Gas
HHBSN Household battery storage
HPHSN Hydro pumped storage
HRDCN Anthracite or hard coal
HVELEC High-voltage electricity
HYDN Hydrogen
LIGN Lignite
LNGN liquefied natural gas
LVELEC Low-voltage electricity
NUCLN Uranium
OILN Oil
WSTN Waste

Other components

Abbreviation Full Name
co2Price Price of emitting one unit of CO2 into the atmosphere
r Interest rate
HV_LOAD Wholesale market load (high voltage level)
LV_LOAD Building load (low/medium voltage level)

Custom constraints (defined in the config.yaml file)

  • CO2 management
  • energy independence
  • fuel production constraint
  • reserve margin
  • renewable generation share constraint
  • must run constraint of thermal generators
  • capacity factor constraint

You can refer to Model builder constraints for more information.