Mga fix/0.4.0

CHANGELOG.md

@@ -43,6 +43,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   non-zero in multi-stage GenX (#666)
 - Added condition number scaling added to objective function (#667)
 - Added versioned doc-pages for v0.3.6 and v0.4.0
+- Add constraint in mga to compute total capacity in each zone from a given technology type (#681)
+- New settings parameter MGAAnnualGeneration to switch between different MGA formulations (#681)
 
 ### Fixed
 - Set MUST_RUN=1 for RealSystemExample/small_hydro plants (#517).

docs/src/User_Guide/generate_alternatives.md

@@ -4,12 +4,10 @@ GenX includes a modeling to generate alternatives (MGA) package that can be used
 
 1. Add a `Resource_Type` column in all the resource `.csv` files denoting the type of each technology.
 2. Add a `MGA` column in all the resource `.csv` files denoting the availability of the technology.
-3. Set the `ModelingToGenerateAlternatives` flag in the `GenX_Settings.yml` file to 1.
-4. Set the `ModelingtoGenerateAlternativeSlack` flag in the `GenX_Settings.yml` file to the desirable level of slack.
-5. Create a `Rand_mga_objective_coefficients.csv` file to provide random objective function coefficients for each MGA iteration.
+3. Set the `ModelingToGenerateAlternatives` flag in the `genx_settings.yml` file to 1.
+4. Set the `ModelingtoGenerateAlternativeSlack` flag in the `genx_settings.yml` file to the desirable level of slack.
+5. Set the `ModelingToGenerateAlternativesIterations` flag to half the total number of desired solutions, as each iteration provides 2 solutions.
+6. Set the `MGAAnnualGeneration` flag in the `genx_settings.yml` file to the desired MGA formulation.
+7. Solve the model using `Run.jl` file.
 
-For each iteration, number of rows in the `Rand_mga_objective_coefficients`.csv file represents the number of distinct technology types while number of columns represent the number of model zones.
-
-Solve the model using `Run.jl` file.
-
-Results from the MGA algorithm would be saved in MGA_max and MGA_min folders in the `Example_Systems/` folder.
+Results from the MGA algorithm would be saved in MGA_max and MGA_min folders in the case folder.

src/additional_tools/modeling_to_generate_alternatives.jl

@@ -3,20 +3,35 @@
 
 We have implemented an updated Modeling to Generate Alternatives (MGA) Algorithm proposed by [Berntsen and Trutnevyte (2017)](https://www.sciencedirect.com/science/article/pii/S0360544217304097) to generate a set of feasible, near cost-optimal technology portfolios. This algorithm was developed by [Brill Jr, E. D., 1979](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.25.5.413) and introduced to energy system planning by [DeCarolia, J. F., 2011](https://www.sciencedirect.com/science/article/pii/S0140988310000721).
 
-To create the MGA formulation, we replace the cost-minimizing objective function of GenX with a new objective function that creates multiple generation portfolios by zone. We further add a new budget constraint based on the optimal objective function value $f^*$ of the least-cost model and the user-specified value of slack $\delta$. After adding the slack constraint, the resulting MGA formulation is given as:
+To create the MGA formulation, we replace the cost-minimizing objective function of GenX with a new objective function that creates multiple generation portfolios by zone. We further add a new budget constraint based on the optimal objective function value $f^*$ of the least-cost model and the user-specified value of slack $\delta$. After adding the slack constraint, the resulting MGA formulation is given as (`MGAAnnualGeneration = 0` in the genx_settings.yml file, or not set):
 
 ```math
 \begin{aligned}
 	\text{max/min} \quad
 	&\sum_{z \in \mathcal{Z}}\sum_{r \in \mathcal{R}} \beta_{z,r}^{k}P_{z,r}\\
 	\text{s.t.} \quad
-	&P_{zr} = \sum_{y \in \mathcal{G}}\sum_{t \in \mathcal{T}} \omega_{t} \Theta_{y,t,z,r}  \\
+	&P_{z,r} = \sum_{y \in \mathcal{G}}C_{y,z,r}  \\
 	& f \leq f^* + \delta \\
 	&Ax = b
 \end{aligned}
 ```
 
-where, $\beta_{zr}$ is a random objective fucntion coefficient betwen $[0,100]$ for MGA iteration $k$. $\Theta_{y,t,z,r}$ is a generation of technology $y$ in zone $z$ in time period $t$ that belongs to a resource type $r$. We aggregate $\Theta_{y,t,z,r}$ into a new variable $P_{z,r}$ that represents total generation from technology type $r$ in a zone $z$. In the second constraint above, $\delta$ denote the increase in budget from the least-cost solution and $f$ represents the expression for the total system cost. The constraint $Ax = b$ represents all other constraints in the power system model. We then solve the formulation with minimization and maximization objective function to explore near optimal solution space.
+where, $\beta_{z,r}$ is a random objective function coefficient betwen $[0,1]$ for MGA iteration $k$. We aggregate capacity into a new variable $P_{z,r}$ that represents total capacity from technology type $r$ in a zone $z$. 
+
+If the users set `MGAAnnualGeneration = 1` in the genx_settings.yml file, the MGA formulation is given as:
+```math
+\begin{aligned}
+\text{max/min} \quad
+	&\sum_{z \in \mathcal{Z}}\sum_{r \in \mathcal{R}} \beta_{z,r}^{k}P_{z,r}\\
+	\text{s.t.} \quad
+	&P_{z,r} = \sum_{y \in \mathcal{G}}\sum_{t \in \mathcal{T}} \omega_{t} \Theta_{y,t,z,r}  \\
+	& f \leq f^* + \delta \\
+	&Ax = b
+\end{aligned}
+```
+where, $\beta_{z,r}$ is a random objective function coefficient betwen $[0,1]$ for MGA iteration $k$. $\Theta_{y,t,z,r}$ is a generation of technology $y$ in zone $z$ in time period $t$ that belongs to a resource type $r$. We aggregate $\Theta_{y,t,z,r}$ into a new variable $P_{z,r}$ that represents total generation from technology type $r$ in a zone $z$. 
+
+In the second constraint in both the above formulations, $\delta$ denote the increase in budget from the least-cost solution and $f$ represents the expression for the total system cost. The constraint $Ax = b$ represents all other constraints in the power system model. We then solve the formulation with minimization and maximization objective function to explore near optimal solution space.
 """
 function mga(EP::Model, path::AbstractString, setup::Dict, inputs::Dict)
     if setup["ModelingToGenerateAlternatives"] == 1
@@ -39,29 +54,10 @@ function mga(EP::Model, path::AbstractString, setup::Dict, inputs::Dict)
         # Read slack parameter representing desired increase in budget from the least cost solution
         slack = setup["ModelingtoGenerateAlternativeSlack"]
 
-        ### Variables ###
-
-        @variable(EP, vSumvP[TechTypes = 1:length(TechTypes), z = 1:Z]>=0) # Variable denoting total generation from eligible technology of a given type
-
-        ### End Variables ###
-
         ### Constraints ###
 
         # Constraint to set budget for MGA iterations
         @constraint(EP, budget, EP[:eObj]<=Least_System_Cost * (1 + slack))
-
-        # Constraint to compute total generation in each zone from a given Technology Type
-        function resource_in_zone_with_TechType(tt::Int64, z::Int64)
-            condition::BitVector = (resource_type_mga.(gen) .== TechTypes[tt]) .&
-                                   (zone_id.(gen) .== z)
-            return resource_id.(gen[condition])
-        end
-        @constraint(EP,
-            cGeneration[tt = 1:length(TechTypes), z = 1:Z],
-            vSumvP[tt,
-                z]==sum(EP[:vP][y, t] * inputs["omega"][t]
-                   for y in resource_in_zone_with_TechType(tt, z), t in 1:T))
-
         ### End Constraints ###
 
         ### Create Results Directory for MGA iterations
@@ -87,7 +83,8 @@ function mga(EP::Model, path::AbstractString, setup::Dict, inputs::Dict)
             ### Maximization objective
             @objective(EP,
                 Max,
-                sum(pRand[tt, z] * vSumvP[tt, z] for tt in 1:length(TechTypes), z in 1:Z))
+                sum(pRand[tt, z] * EP[:vMGA][tt, z]
+                for tt in 1:length(TechTypes), z in 1:Z))
 
             # Solve Model Iteration
             status = optimize!(EP)
@@ -101,7 +98,8 @@ function mga(EP::Model, path::AbstractString, setup::Dict, inputs::Dict)
             ### Minimization objective
             @objective(EP,
                 Min,
-                sum(pRand[tt, z] * vSumvP[tt, z] for tt in 1:length(TechTypes), z in 1:Z))
+                sum(pRand[tt, z] * EP[:vMGA][tt, z]
+                for tt in 1:length(TechTypes), z in 1:Z))
 
             # Solve Model Iteration
             status = optimize!(EP)
@@ -117,3 +115,61 @@ function mga(EP::Model, path::AbstractString, setup::Dict, inputs::Dict)
         ### End MGA Iterations ###
     end
 end
+
+@doc raw"""
+    mga!(EP::Model, inputs::Dict, setup::Dict)
+
+This function reads the input data, collect the resources with MGA flag on and creates a set of unique technology types. 
+The function then adds a constraint to the model to compute total capacity in each zone from a given Technology Type.
+
+If the user set `MGAAnnualGeneration = 0` in the genx_settings.yml file, the constraint has the following form:
+```math
+P_{z,r} = \sum_{y \in \mathcal{G}}C_{y,z,r}
+```
+where, the aggregated capacity $P_{z,r}$ represents total capacity from technology type $r$ in a zone $z$. 
+
+If the user set `MGAAnnualGeneration = 1` in the genx_settings.yml file, the constraint has the following form:
+```math
+P_{z,r} = \sum_{y \in \mathcal{G}}\sum_{t \in \mathcal{T}} \omega_{t} \Theta_{y,t,z,r} 
+```
+where $\Theta_{y,t,z,r}$ is a generation of technology $y$ in zone $z$ in time period $t$ that belongs to a resource type $r$. $\Theta_{y,t,z,r}$ is aggregated into a new variable $P_{z,r}$ that represents total generation from technology type $r$ in a zone $z$. 
+
+# Arguments
+- `EP::Model`: GenX model object
+- `inputs::Dict`: Dictionary containing input data
+
+# Returns
+- This function updates the model object `EP` with the MGA variables and constraints in-place.
+"""
+function mga!(EP::Model, inputs::Dict, setup::Dict)
+    println("MGA Module")
+
+    T = inputs["T"]     # Number of time steps (hours)
+    Z = inputs["Z"]     # Number of zones
+    gen = inputs["RESOURCES"]    # Resources data
+    # Read set of MGA variables
+    annual_gen = setup["MGAAnnualGeneration"] ### Choose setting in genx_settings.yaml: MGAAnnualGeneration: 1 = annual generation, otherwise, sum of capacity
+    # Create a set of unique technology types
+    resources_with_mga_on = gen[ids_with_mga(gen)]
+    TechTypes = unique(resource_type_mga.(resources_with_mga_on))
+
+    function resource_in_zone_same_TechType(tt::Int64, z::Int64)
+        condition::BitVector = (resource_type_mga.(gen) .== TechTypes[tt]) .&
+                               (zone_id.(gen) .== z)
+        return resource_id.(gen[condition])
+    end
+    # Constraint to compute total generation in each zone from a given Technology Type
+    ### Variables ###
+    @variable(EP, vMGA[TechTypes = 1:length(TechTypes), z = 1:Z]>=0)
+
+    ### Constraint ###
+    if annual_gen == 1   # annual generation
+        @constraint(EP, cGeneration[tt = 1:length(TechTypes), z = 1:Z],
+            vMGA[tt,z]==sum(EP[:vP][y, t] * inputs["omega"][t]
+            for y in resource_in_zone_same_TechType(tt, z), t in 1:T))
+    else
+        @constraint(EP, cCapEquiv[tt = 1:length(TechTypes), z = 1:Z],
+            vMGA[tt,z]==sum(EP[:eTotalCap][y]
+            for y in resource_in_zone_same_TechType(tt, z)))
+    end
+end

src/case_runners/case_runner.jl

@@ -94,7 +94,7 @@ function run_genx_case_simple!(case::AbstractString, mysetup::Dict, optimizer::A
         println(elapsed_time)
         if mysetup["ModelingToGenerateAlternatives"] == 1
             println("Starting Model to Generate Alternatives (MGA) Iterations")
-            mga(EP, case, mysetup, myinputs, outputs_path)
+            mga(EP, case, mysetup, myinputs)
         end
 
         if mysetup["MethodofMorris"] == 1

src/configure_settings/configure_settings.jl

@@ -18,6 +18,7 @@ function default_settings()
         "TimeDomainReductionFolder" => "TDR_results",
         "ModelingToGenerateAlternatives" => 0,
         "ModelingtoGenerateAlternativeSlack" => 0.1,
+        "MGAAnnualGeneration" => 0,
         "MultiStage" => 0,
         "MethodofMorris" => 0,
         "IncludeLossesInESR" => 0,

src/model/generate_model.jl

@@ -225,6 +225,10 @@ function generate_model(setup::Dict, inputs::Dict, OPTIMIZER::MOI.OptimizerWithA
         maximum_capacity_requirement!(EP, inputs, setup)
     end
 
+    if setup["ModelingToGenerateAlternatives"] == 1
+        mga!(EP, inputs, setup)
+    end
+
     ## Define the objective function
     @objective(EP, Min, setup["ObjScale"]*EP[:eObj])