Database
Overview
The EnergyPATHWAYS database represents all energy consuming sectors and subsectors of the economy, including residential, commercial, industrial, and transportation energy demands. This includes calibrated energy demand by subsector, and the scenario measures that impact energy demand by changing the way energy is consumed under different policy, technology, feasibility, or economic assumptions. You will make changes to the database to represent different goals and constraints.
Example components of demand scenarios include different rates of electrification of vehicles and appliances, different efficiency assumptions, different service demand assumptions such as adjustments to vehicle miles traveled, and different rates of electrification in industry. These can be used to represent policies that impact the demand side, or uncertainty about outcomes on the demand side due to consumer behavior and/or policy implementation.
Database Column Glossary
- cost_of_capital
Technology cost of capital used in calculating levelized cost outputs.
- currency
Data input currency. Can be any from the currency table.
- currency_year
Year of currency value, used in inflating or deflating costs to a common year.
- extrapolation_method
Method used to extrapolate data to years not explicitly defined in the database. See Timeseries cleaning methods.
- interpolation_method
Method used to interpolate data between given years, can be different than the extrapolation method. See Timeseries cleaning methods.
- input_type
Specifying whether the input data represents an ‘intensity’ or a ‘total’ value.
- notes
Optional user notes for database rows.
- sensitivity
Sensitivity tag for the data input. Used when assigning sensitivity values to a scenario.
- source
Optional specification of the source of the data input.
- unit
Input unit for the data. The pint library is used for unit conversions and most units are supported.
- vintage
Specifies the sales year of a technology.
- year
Specifies the year of the data input.
- value
The value of the data input, must be numerical.
- final_energy
Specifies the final energy type of the data input. Can be any from the final energy table. Column may be optional.
- subsector
Specifies the subsector of the data input. Can be any from the subsector table. Column may be optional.
- demand_technology
Specifies the demand technology for energy or service demand. Column may be optional.
- min_lifetime
Minimum lifetime of a technology in years. User must either specify the min & max lifetime or the mean & lifetime variance.
- max_lifetime
Maximum lifetime of a technology in years. User must either specify the min & max lifetime or the mean & lifetime variance.
- mean_lifetime
Average lifetime of a technology in years. User must either specify the min & max lifetime or the mean & lifetime variance.
- lifetime_variance
Lifetime variance of a technology in years. User must either specify the min & max lifetime or the mean & lifetime variance.
- shape
Hourly shape of the service demand for a sector, subsector, or technology.
- linked
Technology that the current technology is linked to. For example, a heat pump that provides heating is also linked to a heat pump that provides cooling.
- stock_link_ratio
Allows the user to change the ratio of stock between linked technologies.
- driver_1
Energy demand driver useful for extrapolating demand. Can be any from the driver table. Multiple drivers can be used together to create a composite.
- driver_2
Energy demand driver useful for extrapolating demand. Can be any from the driver table. Multiple drivers can be used together to create a composite.
- driver_3
Energy demand driver useful for extrapolating demand. Can be any from the driver table. Multiple drivers can be used together to create a composite.
- driver_denominator_1
Indicates that the input data has been normalized by an underlying driver. For example, light duty service demand may be input as annual vehicle miles traveled per person. In this case, the driver denominator would be ‘population’.
- driver_denominator_2
Indicates that the input data has been normalized by an underlying driver. For example, light duty service demand may be input as annual vehicle miles traveled per person. In this case, the driver denominator would be ‘population’.
- base_driver
Used in the DemandDrivers table to help extrapolate drivers. For example, a driver of residential floor area may have a base driver of households, which may have a base driver of population.
- gau
Stands for ‘geographical analysis unit’ and is the term used to reference a single zone (or element) within a geographical category. For example, within a geography of state, each individual state is a gau.
- geography
Specifies the geographical category of the data input.
- geography_map_key
Used to upscale or downscale data inputs between geographies. See Geographies.
- is_stock_dependent
Used in the service demand table to specify whether total service demand is dependent on the size of the stock (True/False).
- is_service_demand_dependent
Used to specify whether energy demand or the size of a stock is dependent on the total size of the service demand (True/False).
- other_index_1
Optional additional index category to maintain during calculations. For example, in the residential sector, building type may be maintained as an additional level of granularity.
- other_index_2
Optional additional index category to maintain during calculations. For example, in the residential sector, building type may be maintained as an additional level of granularity.
- oth_1
One element from the other_index_1 category. If other_index_1 is ‘building_type’, oth_1 may be ‘detached single family’.
- oth_2
One element from the other_index_1 category. If other_index_1 is ‘building_type’, oth_1 may be ‘detached single family’.
Measures
EnergyPATHWAYS operates at its simplest on the level of measures and scenarios. Scenarios are an aggregation of measures, defined here as actions undertaken to change the energy system from a business-as-usual projection.
On the demand side, we employ the following six types of measures:
- Sales Share Measures (database table:
DemandSalesShareMeasures
)These measures change the deployment of technologies using the concept of sales shares. If we wanted to change the penetration of electric vehicles (EVs) into the market, we could develop a measure that creates any level of adoption we want. These measures can be used in any subsectors with technology-level stock representation.
- Stock Measures (database table:
DemandStockMeasures
)These measure also change the deployment of technologies using the concept of stock instead of sales. While equipment sales are the equipment put into service in a specific year and thus of a certain vintage, equipment stock are all stocks that are operating in a certain year of a variety of vintages. If we want to say that the sum of all EVs on the road in 2030 will equal 1.5 million, we would do so with a stock measure. These measures can be used in any subsectors with technology-level stock representation.
- Service Demand Measures (database table:
DemandServiceDemandMeasures
)These measures alter the projection of service demand in a demand subsector. For example, if we projected a 25% decline in vehicle miles traveled due to land-use and ridesharing policies, we could enter that as a measure here.
- Energy Efficiency Measures (database table:
DemandEnergyEfficiencyMeasures
)Energy efficiency measures are used in demand subsectors where we do not have technology and stock-level representation of end-use equipment. We can change the trajectory of energy demand, however, by implementing generic energy efficiency measures that can achieve energy reductions at a specified cost.
- Fuel-Switching Measures (database table:
DemandFuelSwitchingMeasures
)Fuel switching measures are also used in demand subsectors without technology-level representations. We can implement these measures to change the composition of final energy demand. For example, if we wanted to change the final energy type in process heating from pipeline gas to electricity, we could do so with a fuel–switching measure.
Geographies
Three input tables govern the geographies used in EnergyPATHWAYS. These are Geographies, GeographyMapKeys, and GeographiesSpatialJoin. The first two files reference the GeographiesSpatialJoin table and help identify and validate the columns. When creating a database for a new location, the GeographiesSpatialJoin table is often the very first file that gets updated.