multistage process simulation
BaiYansong-bys opened this issue · comments
Hi!
If I want to simulate an online scheduling process with 20 stages, the SDDP model is built as LinearPolicyGraph ,pw_s is the stochastic variables.
the stochastic process happened of 20 stages is as follows,
Stage 1 pw_s = [0, 0];
Stage 2 pw_s = [-0.1, -0.3];
Stage 3 pw_s = [-0.2, -0.3];
.......
Stage 20 pw_s = [-0.01, -0.23];
the online simulation code is as follows:
sampling_scheme =
SDDP.OutOfSampleMonteCarlo(model) do stage
if stage==1
return [SDDP.Noise((pw_s = [0, 0]), 1)]
elseif stage==2
return [SDDP.Noise((pw_s = [-0.1, -0.3]), 1)]
elseif stage==3
return [SDDP.Noise((pw_s = [-0.2, -0.3]), 1)]
.....
elseif stage==20
return [SDDP.Noise((pw_s = [-0.01, -0.23]), 1)]
end
end
simulations = SDDP.simulate(model, 1, sampling_scheme = sampling_scheme)
However, the codes above seem to be incorrect, it tells me SDDP.noise() is used incorrect. how can I express the simulation process?
How have you defined SDDp.parameterize.
Perhaps you just need return [SDDP.Noise([0, 0], 1)]
Thanks a lot, Oscar.
My SDDP.parameterize() is as follows,
SDDP.parameterize(sp, sce0, Pro0) do omga#sce0,Pro0
#JuMP.fix.(pw_s, omga)
#for i in 1:NW
set_normalized_coefficient(c_stochastic1, uw[1].out, -pw_l[1,stage]*(1+omga[1]))
set_normalized_coefficient(c_stochastic2, uw[2].out, -pw_l[2,stage]*(1+omga[2]))
#
#end
end
@constraint(sp, c_stochastic1, pw[1]<=uw[1].out)
@constraint(sp, c_stochastic2, pw[2]<=uw[2].out)
If I write this below,
sampling_scheme =
SDDP.OutOfSampleMonteCarlo(model) do stage
if stage==1
return [SDDP.Noise([0, 0], 1.0)]
else
return [SDDP.Noise([-0.01, -0.23], 1.0)]
end
end
simulations = SDDP.simulate(model, 1, sampling_scheme = sampling_scheme)
It tells me the error:
ERROR: TypeError: in typeassert, expected Vector{SDDP.Noise{Int64}}, got a value of type Vector{SDDP.Noise{Vector{Float64}}}
Stacktrace:
What is sce0? You need to make sure that the element type of sce0 is the same type that you pass to `OutOfSample.
In this case, your OutOfSample is being passed a Vector{Float64}, but it expected an Int64.
It's hard to say anything more without a reproducible example.
Sorry, i have not expressed the sce0 clearly. the codes are as follows,
using PowerModels, XLSX, SDDP, Gurobi
NW = 2;
NT = 20;
pw_l = [112.1 108.294 104.89 102.436 101.142 99.996 98.806 97.212 97.63 98.194 98.904 100.054 107.484 115.38 123.82 135.202 141.516 148.648 156.178 169.37; 102.32 99.172 92.428 82.89 75.532 72.61 74.672 83.732 90.016 97.094 103.202 109.412 115.218 129.026 135.84 131.632 129.756 127.258 123.796 121.802]
model = SDDP.LinearPolicyGraph(
stages = 20,
sense = :Min,
lower_bound = -800000,#-800000
optimizer = Gurobi.Optimizer,
#integrality_handler=SDDP.LagrangianDuality(),
) do sp, stage
@variables(sp, begin
pw[1:NW] >= 0
end)
@variable(sp,uw[1:NW],Bin)
if stage == 1
@constraint(
sp,
[i in 1:NW],
pw[i] <= pw_l[i, stage] * (1.0) * uw[i]
)
elseif stage in collect(2:NT)
SDDP.parameterize(sp, [[0.2,0.3],[0,-0.1]],[0.5,0.5]) do omga
for i = 1:NW
set_normalized_coefficient(
c_stochastic[i],
uw[i],
-pw_l[i, stage] * (1 + omga[i]),
)
#
end
end
@constraint(sp, c_stochastic[i in 1:NW], pw[i] <= uw[i])
end
@stageobjective(
sp,
-sum(pw)
)
set_silent(sp)
end
SDDP.train(
model,
iteration_limit = 50,
duality_handler = SDDP.ContinuousConicDuality(),
)#,log_frequency = 100
sampling_scheme = SDDP.OutOfSampleMonteCarlo(model) do stage
if stage == 1
return [SDDP.Noise([0.0, 0.0], 1.0)]
else
return [SDDP.Noise([-0.01, -0.23], 1.0)]
end
end
simulations = SDDP.simulate(model, 1, sampling_scheme = sampling_scheme)
The following shows the error report：
LoadError: TypeError: in typeassert, expected Vector{SDDP.Noise{Int64}}, got a value of type Vector{SDDP.Noise{Vector{Float64}}}
Stacktrace:
Your example doesn't have any state variables?
The original code has the state varaibles, I have similified it, the following code has the state variables:
using Gurobi, SDDP
NW = 2;
NT = 20;
NB = 30;
W_S = [3,16];
pw_l = [112.1 108.294 104.89 102.436 101.142 99.996 98.806 97.212 97.63 98.194 98.904 100.054 107.484 115.38 123.82 135.202 141.516 148.648 156.178 169.37; 102.32 99.172 92.428 82.89 75.532 72.61 74.672 83.732 90.016 97.094 103.202 109.412 115.218 129.026 135.84 131.632 129.756 127.258 123.796 121.802]
ini_ui = zeros(NB);
model = SDDP.LinearPolicyGraph(
stages = 20,
sense = :Min,
lower_bound = -800000,#-800000
optimizer = Gurobi.Optimizer,
#integrality_handler=SDDP.LagrangianDuality(),
) do sp, stage
@variable(sp, uw[1:NW], Bin)
@variable(sp, pw[1:NW] >= 0)
@variable(
sp,
0 <= ui[i in 1:NB] <= 1,
SDDP.State,
Bin,
initial_value = ini_ui[i]
)
@constraint(sp, [i in 1:NW], uw[i] <= ui[W_S[i]].out)
@constraint(sp, [i in 1:NB], ui[i].out >= ui[i].in)
if stage == 1
@constraint(
sp,
[i in 1:NW],
pw[i] <= pw_l[i, stage] * (1.0) * uw[i]
)
elseif stage in collect(2:NT)
SDDP.parameterize(sp, [[0.2,0.3],[0,-0.1]],[0.5,0.5]) do omga
for i = 1:NW
set_normalized_coefficient(
c_stochastic[i],
uw[i],
-pw_l[i, stage] * (1 + omga[i]),
)
#
end
end
@constraint(sp, c_stochastic[i in 1:NW], pw[i] <= uw[i])
end
@stageobjective(
sp,
-sum(pw)
)
set_silent(sp)
end
SDDP.train(
model,
iteration_limit = 50,
duality_handler = SDDP.ContinuousConicDuality(),
)#,log_frequency = 100
sampling_scheme = SDDP.OutOfSampleMonteCarlo(model) do stage
if stage == 1
return [SDDP.Noise([0.0, 0.0], 1.0)]
else
return [SDDP.Noise([-0.01, -0.23], 1.0)]
end
end
simulations = SDDP.simulate(model, 1, sampling_scheme = sampling_scheme)
It can work, but SDDP.Noise has the same error.
You need to use
sampling_scheme = SDDP.OutOfSampleMonteCarlo(
model;
use_insample_transition = true,
) do stage
if stage == 1
return [SDDP.Noise(nothing, 1.0)]
else
return [SDDP.Noise([-0.01, -0.23], 1.0)]
end
endThanks a lot, Oscar. The code can run very well.