Documentation for master

.travis.yml

@@ -17,15 +17,15 @@ jobs:
       julia: 1.0
       os: linux
       script:
-        - julia --project=docs/ -e ' using Pkg;  Pkg.add(PackageSpec(url="https://github.com/holgerteichgraeber/ClustForOpt.jl", rev="dev"))'
+        - julia --project=docs/ -e ' using Pkg;  Pkg.add(PackageSpec(url="https://github.com/holgerteichgraeber/ClustForOpt.jl"))'
         - julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd()));  Pkg.instantiate()'
         - julia --project=docs/ docs/make.jl
       after_success:
     - stage: "Testing"
       julia: 1.0
       os: linux
       script:
-        - julia --project=test/ -e ' using Pkg; Pkg.add(PackageSpec(url="https://github.com/holgerteichgraeber/ClustForOpt.jl", rev="dev"))'
+        - julia --project=test/ -e ' using Pkg; Pkg.add(PackageSpec(url="https://github.com/holgerteichgraeber/ClustForOpt.jl"))'
         - julia --project=test/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd()));  Pkg.instantiate()'
         - julia --project=test/ test/cep.jl
       after_success:

README.md

@@ -30,14 +30,22 @@ This package is developed by Elias Kuepper [@YoungFaithful](https://github.com/y
 
 ## Installation
 This package runs under julia v1.0 and higher.
-Install using:
+First install the dependencies:
+- `JuMP.jl` - for the modeling environment
+- `StatsKit.jl` - for handling of `.csv`-Files
+- `JLD2` - for saving your result data
+- `FileIO` - for file accessing
+- `ClustForOpt.jl` - for time-series data
 
 ```julia
-]
-add https://github.com/YoungFaithful/CEP.jl.git
+using Pkg
+Pkg.add(["JuMP","StatsKit","JLD2","FileIO"])
+Pkg.add(PackageSpec(url="https://github.com/holgerteichgraeber/ClustForOpt.jl"))
+```
+Second install `CEP.jl`:
+```julia
+Pkg.add(PackageSpec(url="https://github.com/YoungFaithful/CEP.jl.git"))
 ```
-where `]` opens the julia package manager.
-
 ## Example Workflow
 ```julia
 using CEP

docs/make.jl

@@ -22,4 +22,4 @@ makedocs(sitename="CEP.jl",
         "assets/preparing_clust_data_load",
         "assets/preparing_clust_data_agg"])
 
-deploydocs(repo = "github.com/YoungFaithful/CEP.jl.git", devbranch = "dev")
+deploydocs(repo = "github.com/YoungFaithful/CEP.jl.git")

docs/src/csv_structure.md

@@ -47,10 +47,14 @@ The package provides data for:
 
 ### techs.csv
 !!! note
+    The currently supported `categ` are
+    - `generation`: For generation technologies that are either dispatchable (`none` in column `time_series`) or non-dispatchable (`time_series_name` in column `time_series`)
+    - `transmission`: For transmission technologies that have no capacity (`CAP`) per `node`, but capacities (`TRANS`)  per `line`
+    - `storage_e`,`storage_in`,`storage_out`: For storage technology.
     A storage technology has always three components
-    - `storage_e`: The energy part of the storage device [MWh]
-    - `storage_in`: The power part for charging the storage device [MW]
-    - `storage_out`: The power part for discharging the storage device [MW]
+    - `[storage-name]_e`: The energy part of the storage device [MWh]
+    - `[storage-name]_in`: The power part for charging the storage device [MW]
+    - `[storage-name]_out`: The power part for discharging the storage device [MW]
     If e.g. in a lithium-ion battery the `storage_in` should be the same as `storage_out`, just set the `cap` costs in `costs.csv` of either `storage_in` or `storage_out` to zero. This will add a constraint to bind their capacities.
 
 |`tech`|`categ`|`sector`|`fuel`|`eff`|`max_gradient`|`time_series`|`lifetime`|`financial_lifetime`|`discount_rate`|

docs/src/index.md

@@ -30,10 +30,19 @@ This package is developed by Elias Kuepper [@YoungFaithful](https://github.com/y
 
 ## Installation
 This package runs under julia v1.0 and higher.
-Install using:
+First install the dependencies:
+- `JuMP.jl` - for the modeling environment
+- `StatsKit.jl` - for handling of `.csv`-Files
+- `JLD2` - for saving your result data
+- `FileIO` - for file accessing
+- `ClustForOpt.jl` - for time-series data
 
 ```julia
-]
-add https://github.com/YoungFaithful/CEP.jl.git
+using Pkg
+Pkg.add(["JuMP","StatsKit","JLD2","FileIO"])
+Pkg.add(PackageSpec(url="https://github.com/holgerteichgraeber/ClustForOpt.jl"))
+```
+Second install `CEP.jl`:
+```julia
+Pkg.add(PackageSpec(url="https://github.com/YoungFaithful/CEP.jl.git"))
 ```
-where `]` opens the julia package manager.

docs/src/opt_cep.md

@@ -91,18 +91,17 @@ OptResult
     The model tracks how it is setup and which equations are used. This can help you to understand the models exact configuration without looking up the source code.
 
 The information of the model setup can be checked out the following way:
-```@setup optinfo
+```@setup 3
 using CEP
 using Clp
 optimizer=Clp.Optimizer
 state="GER_1"
 years=[2016]
 ts_input_data = load_timeseries_data_provided(state;T=24, years=years)
 cep_data = load_cep_data_provided(state)
-## CLUSTERING ##
 ts_clust_data = run_clust(ts_input_data;method="kmeans",representation="centroid",n_init=10,n_clust=5).best_results
 ```
-```@example optinfo
+```@example 3
 result = run_opt(ts_clust_data,cep_data,optimizer;descriptor="Model Name")
-result.opt_info["model"]
+println.(result.opt_info["model"])
 ```

docs/src/workflow.md

@@ -6,7 +6,7 @@ The workflow for this package can be broken down to:
 ## Data Preparation
 The CEP needs two types of data
 - Time series data in the type `ClustData` - [Preparing ClustData](@ref)
-- Cost, node, (line), and technology data in the type `OptDataCEP` - [Preparing OptDataCE](@ref)
+- Cost, node, (line), and technology data in the type `OptDataCEP` - [Preparing OptDataCEP](@ref)
 They are kept separate as just the time series dependent data is used to determine representative periods (clustering).
 
 ![Plot](assets/workflow.svg)

src/utils/datastructs.jl

@@ -28,7 +28,15 @@ struct OptVariable{T,N,Ax,L<:NTuple{N,Dict}} <: AbstractArray{T,N}
     type::String
 end
 
-"OptResult"
+"""
+      OptResult{status::Symbol,objective::Float64,variables::Dict{String,Any},sets::Dict{String,Array},opt_config::Dict{String,Any},opt_info::Dict{String,Any}}
+- `status`: Symbol about the solution status of the model in normal cases `:OPTIMAL`
+- `objective`: Value of the objective function
+- `variables`: Dictionary with each OptVariable as an entry
+- `sets`: Dictionary with each set as an entry
+- `opt_config`: The configuration of the model setup - for more detail see tye `run_opt` documentation that sets the `opt_config` up
+- `opt_info`: Holds information about the model. E.g. `opt_info["model"]` contains the exact equations used in the model. 
+"""
 struct OptResult
  status::Symbol
  objective::Float64
@@ -40,11 +48,11 @@ end
 
 """
      OptDataCEP{region::String, costs::OptVariable, techs::OptVariable, nodes::OptVariable, lines::OptVariabl} <: OptData
--`region::String`          name of state or region data belongs to
--`costs::OptVariable`    costs[tech,node,year,account,impact] - Number
--`techs::OptVariable`    techs[tech] - OptDataCEPTech
--`nodes::OptVariable`    nodes[tech, node] - OptDataCEPNode
--`lines::OptVarible`     lines[tech, line] - OptDataCEPLine
+- `region::String`:          name of state or region data belongs to
+- `costs::OptVariable`:    costs[tech,node,year,account,impact] - Number
+- `techs::OptVariable`:    techs[tech] - OptDataCEPTech
+- `nodes::OptVariable`:    nodes[tech, node] - OptDataCEPNode
+- `lines::OptVarible`:     lines[tech, line] - OptDataCEPLine
 instead of USD you can also use your favorite currency like EUR
 """
 struct OptDataCEP <: OptData

src/utils/utils.jl

@@ -121,7 +121,7 @@ function get_cep_variable_set(scenario::Scenario,
 end
 
 """
-    get_cep_design_variables(opt_result::OptResult; capacity_factors::Dict{String,Number}=Dict{String,Number}())
+    get_cep_design_variables(opt_result::OptResult)
 Returns all design variables in this opt_result matching the type "dv"
 Additionally you can add capacity factors, which scale the design variables by multiplying it with the value in the Dict
 """