In olugovoy/energyRt: Energy systems modeling toolbox in R, development version

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A minimal working example

An energy system model optimizes technological chains to meet a given (exogenous) demand for a commodity (e.g. electricity, heat, etc.) using given resources (e.g. fuels, renewable energy sources, etc.) and available technological options (e.g. power plants, heat pumps, etc.). The minimal model must have a definition of the final demand and available resources.\ Let's start with the package settings and continue with the minimal model definition.\

library(energyRt)

library(tidyverse) # for data manipulation and visualization

Check which solver is set

my_solver <- get_default_solver()
print(my_solver)

(Optional) change default solver.

set_default_solver(solver_options$julia_highs)

# Check if solver is set and available
# check_solver()

Check/set directory to store scenarios using the set_scenarios_path and get_scenarios_path functions.\

get_scenarios_path()

The following minimal model consists of a commodity, a supply, and a demand object.\ The commodity object is defined using the newCommodity constructor-function. The commodity object has a name and a description.\

# define a commodity
COM <- newCommodity("COM")

The supply object is defined using the newSupply constructor-function. The supply object has a name, a description, a commodity it supplies, and availability data. The availability data is a data frame with a time-series of costs or prices of the commodity.\

# Declare a domestic supply (market) for the commodity
SUP <- newSupply(
  name = "SUP", 
  desc = "Domestic supply of commodity",
  commodity = "COM",
  availability = data.frame(
    cost = 10 # costs or prices of the commodity
    )
  )

This simple supply object sets no physical limits on the availability of the commodity. Only the price is set.\

Schematic representation of the supply object shows an output arrow with the name of the supply object and the commodity it supplies. No inputs are required to "produce" the commodity in the model.\

draw(SUP)

Demand is an opposite object to supply. It represents the final consumption of a commodity in the model.

DEM <- newDemand(
  name = "DEM",
  desc = "Final demand for commodity",
  commodity = "COM",
  dem = data.frame(
    slice = "ANNUAL", # time-slices are defined in the calendar object
    dem = 100
    )
  )
draw(DEM)

The minimal model can be built now from the created objects. There is a commodity that is supplied and demanded. The commodity has a price, and the demand is exogenously set. The optimization problem is quite simple: minimize the costs of supplying the demand. No technological alternatives are available yet in the model to choose from. No constraints are set on resources.\

Let's create a model object and solve it. Several settings are required to parameterize the model and create a model object:\ - region - a name of a geographical region where the model is defined.\ - data - a repository object that stores all data objects used in the model.\ - calendar - a calendar object that defines the time structure of the model.\ - horizon - a horizon object that defines the time horizon of the model.\ - discount - a discount rate used to compute the present value of costs in different time periods.\

The repository object is created by combining all data objects used in the model in all scenarios or a group of scenarios. Additional data (model objects and settings) can be added to a particular scenario.\

# create a repository object to store the core model data
world_repo <- newRepository(name = "world_repo", COM, SUP, DEM)
# check content of the repository by type/class
summary(world_repo)
# check names of the repository objects
names(world_repo)

The calendar object defines the time structure of the model: time-frames and time-slices. Time-frames are names of time periods that include time-slices. Time-slices are minimal time units in a particular time-frame. Time-frames have nested structure. The top level is a year, with name ANNUAL. This is a default and if no sub-anual resolutions are set, the simplest time structure.\

hello_calendar <- newCalendar() # default calendar with no sub-annual resolutions

Horizon object defines the time horizon of the model. It is a sequence of milestone years and periods for each milestone. The example below defines a planning horizon from 2025 to 2050 with 5-year intervals.\ One-year horizon:

planning_2025 <- newHorizon(2025)
planning_2025

Multi-year horizon:

planning_horizon <- newHorizon(
  period = 2025:2050, 
  intervals = rep(5, 10)
  )
planning_horizon

The newModel constructor-function combines all the configuration and data objects to create a model object.\

mod <- newModel(
  name = "MODEL", 
  region = "WORLD",
  data = world_repo,
  calendar = hello_calendar,
  horizon = planning_2025,
  discount = 0.05
  )
class(mod)

Once the model object is created, it can be solved using the solve function. The solved (or interpolated) model object becomes a scenario object.\

# solve for one year
scen_2025 <- solve(mod, name = "HELLO_2025")
summary(scen_2025)

Solving for multiple years assumes the perferct foresight of the all given data for the future. Since we haven't defined any years for the data, it is extrapolated by default for demand and supply objects.\

# solve for multi-year horizon by adding the horizon object
scen_2050 <- solve(mod, name = "HELLO_WORLD", 
                   calendar = planning_horizon)
summary(scen_2050)

tbc...

olugovoy/energyRt documentation built on Nov. 21, 2024, 2:24 a.m.