Dam reservoir management in Julia.



Project Status: Inactive – The project has reached a stable, usable state but is no longer being actively developed; support/maintenance will be provided as time allows. The MIT License

ReservoirManagement.jl is a Julia package that can be used to manage reservoirs (such as dams) using mathematical optimisation tools. Its design should allow easy extensibility for new use cases and kinds of management. For now, it focuses on determining rule curves, based on inflow to the reservoir and purpose needs.


This package relies on mathematical optimisation, and thus requires a solver to be installed. For most features, a simple LP solver is enough (in rare cases, a MIP solver is required). Any solver that implements the MathProgBase interface works (see JuliaOpt's website for more information).

Caution: For now, Gurobi is hardcoded into the source code, occurrences of GurobiSolver() must be replaced by the corresponding solver object. This will be addressed in a near release.

For some statistical operations, this package uses unpublished Julia packages, which thus need to be installed separately (namely, ExtremeValueDistributions.jl for fitting extreme value distributions). Execute those commands from a Julia shell:


Pkg.checkout("ExtremeValueDistributions", "develop")


using ReservoirManagement
using SIUnits
using SIUnits.ShortUnits

The first step is to define the topology of the reservoir: the tributaries. This package defines two kinds of rivers: those that directly flow into the reservoir (the most common case), represented by NaturalRiver, and diverted rivers (whose contribution can be decided up to some level), DivertedRiver.

The main information stored for each river is a series of inflow scenarios, each scenario being represented by an array of time series (whose type is TimeArray).

For example, the Vesdre reservoir has two main tributaries (the rivers Vesdre and Getzbach), plus one diverted river (Helle, which has a minimum environmental flow that must remain in the river after the diversion):

Vesdre = NaturalRiver(name="Vesdre", scenarios=scenariosVesdre)
Getzbach = NaturalRiver(name="Getzbach", scenarios=scenariosGetzbach)
Helle = DivertedRiver(name="Vesdre", scenarios=scenariosHelle, environmental_flow=0.5m^3/s, maximum_flow=15.0m^3/s)

Once this topology is modelled, a reservoir can be defined. The main parameters are:

  • the purposes of the reservoir (i.e. its need for water); for this example, mostly drinking water
  • the outputs from the reservoir (i.e. how much water it can release); for this example, mostly the bottom outlets
  • the minimum and maximum allowable levels purpose = DeterministicPurposes(drinkingWater=0.5m^3/s) out = ConstantDamOutputs(bottomOutlets=100m^3/s) VesdreReservoir = Reservoir(name="Vesdre", capacity=(2500000.m^3, 25000000.m^3), purposes=purpose, outputs=out, rivers_in=[Vesdre, Getzbach], rivers_diverted=[Helle])

The setup phase is done: with this Reservoir data structure, this package can provide optimisation results with the report() function. For now, it focuses on the determination of minimum rule curves (that span over ruleDuration) that guarantee the purposes for some duration (guarantee). If provided, it can compare its results to the current rule curve (currentRuleCurve). The results are mainly a series of plots (enabled by savePlots=true and saved into saveFolder), but also the intermediate results (enabled by saveIntermediate=true and also saved into saveFolder)

report(VesdreReservoir, ruleDuration=Year(1), guarantee=Year(2), 
       saveFolder="…/outputs/", savePlots=true, saveIntermediate=true)

Remark: The values in this example are not the true ones (albeit they are realistic).

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