ED1.jl

using JuMP
using Cbc
using GLPK
using MathOptInterface
using Interact

const MOI = MathOptInterface

function solve_ed(g_max, g_min, c_g, c_g0, c_w, d, w_f)
    
    #Define the economic dispatch (ED) model
    ed=Model(solver = CbcSolver()) 
    
    # Define decision variables    
    @variable(ed, 0 <= g[i=1:2] <= g_max[i]) # power output of generators
    @variable(ed, 0 <= w  <= w_f ) # wind power injection

    # Define the objective function
    @objective(ed,Min,sum(c_g[i] * g[i] for i in 1:2)+ c_w * w)

    # Define the constraint on the maximum and minimum power output of each generator
    for i in 1:2
        @constraint(ed,  g[i] <= g_max[i]) #maximum
        @constraint(ed,  g[i] >= g_min[i]) #minimum
    end

    # Define the constraint on the wind power injection
    @constraint(ed, w <= w_f)

    # Define the power balance constraint
    @constraint(ed, sum(g[i] for i in 1:2) + w == d)

    # Solve statement
    solve(ed)
    
    # return the optimal value of the objective function and its minimizers
    return JuMP.getvalue(g), JuMP.getvalue(w), w_f-JuMP.getvalue(w), JuMP.getobjectivevalue(ed)
end