Skip to content

Commit 3e5a5bf

Browse files
Bill-BeckerBill-Becker
committed
Add OUU tests to runtests and fix one existing test
1 parent 3e75561 commit 3e5a5bf

File tree

5 files changed

+390
-13
lines changed

5 files changed

+390
-13
lines changed

‎test/runtests.jl‎

Lines changed: 8 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1834,7 +1834,7 @@ else # run HiGHS tests
18341834
for ts in p.time_steps
18351835
#heating and cooling loads only
18361836
if ts % 2 == 0 #in even periods, there is a nonzero load and energy is higher cost, and storage should discharge
1837-
p.s.electric_load.loads_kw[ts] = 10
1837+
p.loads_kw_by_scenario[1][ts] = 10
18381838
p.s.dhw_load.loads_kw[ts] = 5
18391839
p.s.space_heating_load.loads_kw[ts] = 5
18401840
p.s.cooling_load.loads_kw_thermal[ts] = 10
@@ -1843,7 +1843,7 @@ else # run HiGHS tests
18431843
p.s.electric_tariff.energy_rates[ts, tier] = 100
18441844
end
18451845
else #in odd periods, there is no load and energy is cheaper - storage should charge
1846-
p.s.electric_load.loads_kw[ts] = 0
1846+
p.loads_kw_by_scenario[1][ts] = 0
18471847
p.s.dhw_load.loads_kw[ts] = 0
18481848
p.s.space_heating_load.loads_kw[ts] = 0
18491849
p.s.cooling_load.loads_kw_thermal[ts] = 0
@@ -4438,5 +4438,11 @@ else # run HiGHS tests
44384438
end
44394439
end
44404440

4441+
@testset "OUU" begin
4442+
include("test_ouu_foundation.jl")
4443+
include("test_ouu_run.jl")
4444+
include("test_monte_carlo_ouu.jl")
4445+
end
4446+
44414447
end
44424448
end

‎test/scenarios/ouu_base.json‎

Lines changed: 21 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,21 @@
1+
{
2+
"Site": {
3+
"latitude": 39.7407,
4+
"longitude": -105.1694
5+
},
6+
"ElectricLoad": {
7+
"doe_reference_name": "LargeHotel",
8+
"annual_kwh": 2000000.0
9+
},
10+
"ElectricTariff": {
11+
"blended_annual_energy_rate": 0.12,
12+
"blended_annual_demand_rate": 15.0
13+
},
14+
"PV": {
15+
"max_kw": 1000.0
16+
},
17+
"ElectricStorage": {
18+
"max_kw": 500.0,
19+
"max_kwh": 2000.0
20+
}
21+
}

‎test/test_monte_carlo_ouu.jl‎

Lines changed: 309 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,309 @@
1+
# using Revise
2+
using JuMP
3+
using HiGHS
4+
# using Xpress
5+
using JSON
6+
using REopt
7+
using Logging
8+
using DotEnv
9+
# using PlotlyJS # Commented out for GitHub Actions
10+
DotEnv.load!()
11+
12+
# ============================================================================
13+
# SOLVER CONFIGURATION - Change solver here
14+
# ============================================================================
15+
const USE_XPRESS = false # Set to false to use HiGHS
16+
17+
"""
18+
Helper function to create model with configured solver
19+
"""
20+
function create_model(mip_gap::Float64=0.05; verbose::Bool=false)
21+
if USE_XPRESS
22+
m = Model(Xpress.Optimizer)
23+
set_optimizer_attribute(m, "OUTPUTLOG", verbose ? 1 : 0)
24+
set_optimizer_attribute(m, "MIPRELSTOP", mip_gap)
25+
else
26+
m = Model(HiGHS.Optimizer)
27+
set_optimizer_attribute(m, "output_flag", verbose)
28+
set_optimizer_attribute(m, "log_to_console", verbose)
29+
set_optimizer_attribute(m, "mip_rel_gap", mip_gap)
30+
end
31+
return m
32+
end
33+
34+
println("\n" * "="^80)
35+
println("Testing Monte Carlo vs Discrete OUU Methods")
36+
println("="^80)
37+
38+
# Suppress info/warning messages
39+
original_logger = Logging.global_logger()
40+
Logging.global_logger(Logging.SimpleLogger(stderr, Logging.Error))
41+
42+
# ============================================================================
43+
# Test 0: Baseline (No Uncertainty) with BAU
44+
# ============================================================================
45+
println("\n" * "─"^80)
46+
println("Test 0: Baseline (No Uncertainty)")
47+
println("─"^80)
48+
println("Single scenario with no uncertainty, comparing BAU vs technology optimization")
49+
50+
scenario_baseline = JSON.parsefile("scenarios/ouu_base.json")
51+
52+
print("Building scenario... ")
53+
s_baseline = Scenario(scenario_baseline)
54+
println("✓")
55+
56+
print("Building REoptInputs... ")
57+
inputs_baseline = REoptInputs(s_baseline)
58+
println("✓")
59+
println(" └─ Number of scenarios: ", inputs_baseline.n_scenarios)
60+
61+
# Create BAU and technology optimization models
62+
m_bau = create_model()
63+
64+
m_tech = create_model()
65+
66+
print("Building and solving models (BAU + Technology Optimization)... ")
67+
results_baseline = run_reopt([m_bau, m_tech], inputs_baseline)
68+
println("✓")
69+
70+
if termination_status(m_bau) == MOI.OPTIMAL && termination_status(m_tech) == MOI.OPTIMAL
71+
println("\n📊 Baseline Results:")
72+
println(" BAU (Business as Usual):")
73+
println(" └─ Objective value: \$", round(objective_value(m_bau), digits=2))
74+
println(" └─ Grid energy supplied: ", round(results_baseline["ElectricUtility"]["annual_energy_supplied_kwh_bau"], digits=1), " kWh")
75+
println("\n Technology Optimal:")
76+
println(" └─ Objective value: \$", round(objective_value(m_tech), digits=2))
77+
println(" └─ Grid energy supplied: ", round(results_baseline["ElectricUtility"]["annual_energy_supplied_kwh"], digits=1), " kWh")
78+
println(" └─ PV size: ", round(results_baseline["PV"]["size_kw"], digits=1), " kW")
79+
println(" └─ Battery power: ", round(results_baseline["ElectricStorage"]["size_kw"], digits=1), " kW")
80+
println(" └─ Battery energy: ", round(results_baseline["ElectricStorage"]["size_kwh"], digits=1), " kWh")
81+
82+
savings = objective_value(m_bau) - objective_value(m_tech)
83+
println("\n Technology Savings: \$", round(savings, digits=2), " (", round(savings/objective_value(m_bau)*100, digits=2), "%)")
84+
else
85+
println("\n⚠️ Baseline model status - BAU: ", termination_status(m_bau), ", Tech: ", termination_status(m_tech))
86+
end
87+
88+
# ============================================================================
89+
# Test 1: Time-Invariant Method (Original Implementation)
90+
# ============================================================================
91+
println("\n" * "─"^80)
92+
println("Test 1: Time-Invariant Method")
93+
println("─"^80)
94+
println("Creates 3 load scenarios × 3 PV scenarios = 9 total scenarios")
95+
println("Each scenario has uniform deviation across all timesteps")
96+
97+
scenario_invariant = JSON.parsefile("scenarios/ouu_base.json")
98+
scenario_invariant["ElectricLoad"]["uncertainty"] = Dict(
99+
"enabled" => true,
100+
"method" => "time_invariant",
101+
"deviation_fractions" => [-0.1, 0.0, 0.1],
102+
"deviation_probabilities" => [0.25, 0.50, 0.25]
103+
)
104+
scenario_invariant["PV"]["production_uncertainty"] = Dict(
105+
"enabled" => true,
106+
"method" => "time_invariant",
107+
"deviation_fractions" => [-0.2, 0.0, 0.2],
108+
"deviation_probabilities" => [0.25, 0.50, 0.25]
109+
)
110+
111+
print("Building scenario... ")
112+
s_invariant = Scenario(scenario_invariant)
113+
println("✓")
114+
115+
print("Building REoptInputs... ")
116+
inputs_invariant = REoptInputs(s_invariant)
117+
println("✓")
118+
println(" └─ Number of scenarios: ", inputs_invariant.n_scenarios)
119+
println(" └─ Scenario probabilities: ", round.(inputs_invariant.scenario_probabilities[1:min(10, end)], digits=4))
120+
121+
m_invariant = create_model()
122+
123+
m_invariant_bau = create_model()
124+
125+
print("Building and solving models (BAU + Technology Optimization)... ")
126+
results_invariant = run_reopt([m_invariant_bau, m_invariant], inputs_invariant)
127+
println("✓")
128+
129+
if termination_status(m_invariant) == MOI.OPTIMAL
130+
println("\n📊 Time-Invariant Method Results:")
131+
println(" Technology Optimal:")
132+
println(" └─ Objective value: \$", round(objective_value(m_invariant), digits=2))
133+
println(" └─ Grid energy supplied: ", round(results_invariant["ElectricUtility"]["annual_energy_supplied_kwh"], digits=1), " kWh")
134+
println(" └─ PV size: ", round(results_invariant["PV"]["size_kw"], digits=1), " kW")
135+
println(" └─ Battery power: ", round(results_invariant["ElectricStorage"]["size_kw"], digits=1), " kW")
136+
println(" └─ Battery energy: ", round(results_invariant["ElectricStorage"]["size_kwh"], digits=1), " kWh")
137+
if termination_status(m_invariant_bau) == MOI.OPTIMAL
138+
println("\n BAU:")
139+
println(" └─ Objective value: \$", round(objective_value(m_invariant_bau), digits=2))
140+
println(" └─ Grid energy supplied: ", round(results_invariant["ElectricUtility"]["annual_energy_supplied_kwh_bau"], digits=1), " kWh")
141+
savings = objective_value(m_invariant_bau) - objective_value(m_invariant)
142+
println(" └─ Technology Savings: \$", round(savings, digits=2), " (", round(savings/objective_value(m_invariant_bau)*100, digits=2), "%)")
143+
end
144+
else
145+
println("\n⚠️ Time-Invariant model status: ", termination_status(m_invariant))
146+
end
147+
148+
# ============================================================================
149+
# Test 2: Monte Carlo Method (New Implementation)
150+
# ============================================================================
151+
println("\n" * "─"^80)
152+
println("Test 2: Monte Carlo Method")
153+
println("─"^80)
154+
println("Creates 9 scenarios (3 load samples × 3 PV samples)")
155+
println("Each scenario has timestep-varying deviations sampled from distribution")
156+
157+
scenario_mc = JSON.parsefile("scenarios/ouu_base.json")
158+
scenario_mc["ElectricLoad"]["uncertainty"] = Dict(
159+
"enabled" => true,
160+
"method" => "discrete",
161+
"deviation_fractions" => [-0.1, 0.0, 0.1],
162+
"deviation_probabilities" => [0.25, 0.50, 0.25],
163+
"n_samples" => 3 # Each sample has different deviation per timestep
164+
)
165+
scenario_mc["PV"]["production_uncertainty"] = Dict(
166+
"enabled" => true,
167+
"method" => "discrete",
168+
"deviation_fractions" => [-0.2, 0.0, 0.2],
169+
"deviation_probabilities" => [0.25, 0.50, 0.25],
170+
"n_samples" => 3
171+
)
172+
173+
print("Building scenario... ")
174+
s_mc = Scenario(scenario_mc)
175+
println("✓")
176+
177+
print("Building REoptInputs... ")
178+
inputs_mc = REoptInputs(s_mc)
179+
println("✓")
180+
println(" └─ Number of scenarios: ", inputs_mc.n_scenarios)
181+
println(" └─ Scenario probabilities (first 5): ", round.(inputs_mc.scenario_probabilities[1:min(5, end)], digits=4))
182+
println(" └─ All equal? ", all(x -> isapprox(x, inputs_mc.scenario_probabilities[1], atol=1e-10), inputs_mc.scenario_probabilities))
183+
184+
m_mc = create_model()
185+
186+
m_mc_bau = create_model()
187+
188+
print("Building and solving models (BAU + Technology Optimization)... ")
189+
results_mc = run_reopt([m_mc_bau, m_mc], inputs_mc)
190+
println("✓")
191+
192+
if termination_status(m_mc) == MOI.OPTIMAL
193+
println("\n📊 Monte Carlo Method Results:")
194+
println(" Technology Optimal:")
195+
println(" └─ Objective value: \$", round(objective_value(m_mc), digits=2))
196+
println(" └─ Grid energy supplied: ", round(results_mc["ElectricUtility"]["annual_energy_supplied_kwh"], digits=1), " kWh")
197+
println(" └─ PV size: ", round(results_mc["PV"]["size_kw"], digits=1), " kW")
198+
println(" └─ Battery power: ", round(results_mc["ElectricStorage"]["size_kw"], digits=1), " kW")
199+
println(" └─ Battery energy: ", round(results_mc["ElectricStorage"]["size_kwh"], digits=1), " kWh")
200+
if termination_status(m_mc_bau) == MOI.OPTIMAL
201+
println("\n BAU:")
202+
println(" └─ Objective value: \$", round(objective_value(m_mc_bau), digits=2))
203+
println(" └─ Grid energy supplied: ", round(results_mc["ElectricUtility"]["annual_energy_supplied_kwh_bau"], digits=1), " kWh")
204+
savings = objective_value(m_mc_bau) - objective_value(m_mc)
205+
println(" └─ Technology Savings: \$", round(savings, digits=2), " (", round(savings/objective_value(m_mc_bau)*100, digits=2), "%)")
206+
end
207+
else
208+
println("\n⚠️ Monte Carlo model status: ", termination_status(m_mc))
209+
end
210+
211+
# ============================================================================
212+
# Comparison
213+
# ============================================================================
214+
if termination_status(m_invariant) == MOI.OPTIMAL && termination_status(m_mc) == MOI.OPTIMAL
215+
println("\n" * "─"^80)
216+
println("Comparison:")
217+
println("─"^80)
218+
219+
pv_diff = results_mc["PV"]["size_kw"] - results_invariant["PV"]["size_kw"]
220+
batt_kw_diff = results_mc["ElectricStorage"]["size_kw"] - results_invariant["ElectricStorage"]["size_kw"]
221+
batt_kwh_diff = results_mc["ElectricStorage"]["size_kwh"] - results_invariant["ElectricStorage"]["size_kwh"]
222+
cost_diff = objective_value(m_mc) - objective_value(m_invariant)
223+
224+
println(" Δ PV size: ", round(pv_diff, digits=1), " kW (", round(pv_diff/results_invariant["PV"]["size_kw"]*100, digits=1), "%)")
225+
println(" Δ Battery power: ", round(batt_kw_diff, digits=1), " kW (", round(batt_kw_diff/results_invariant["ElectricStorage"]["size_kw"]*100, digits=1), "%)")
226+
println(" Δ Battery energy: ", round(batt_kwh_diff, digits=1), " kWh (", round(batt_kwh_diff/results_invariant["ElectricStorage"]["size_kwh"]*100, digits=1), "%)")
227+
println(" Δ Cost: \$", round(cost_diff, digits=2), " (", round(cost_diff/objective_value(m_invariant)*100, digits=2), "%)")
228+
229+
println("\n💡 Key Insight:")
230+
println(" Monte Carlo captures timestep-level uncertainty, while discrete assumes")
231+
println(" all timesteps move together. This can lead to different optimal sizing.")
232+
233+
# ============================================================================
234+
# Visualization: Compare scenario profiles
235+
# ============================================================================
236+
println("\n" * "─"^80)
237+
println("Plotting scenario profiles...")
238+
println("─"^80)
239+
240+
# Plot first 168 hours (1 week) for visibility
241+
# Plotting code commented out for GitHub Actions (PlotlyJS not available)
242+
# plot_hours = 1:min(8760, length(inputs_invariant.time_steps))
243+
#
244+
# # Find baseline scenario (zero deviation) in time_invariant - should be scenario 2 with deviation=0.0
245+
# baseline_scenario_id = 5 # Middle scenario with 0.0 deviation
246+
# mc_scenario_id = 2 # First Monte Carlo scenario
247+
#
248+
# # Create load comparison plot
249+
# load_trace1 = PlotlyJS.scatter(
250+
# x=collect(plot_hours),
251+
# y=inputs_discrete.loads_kw_by_scenario[baseline_scenario_id][plot_hours],
252+
# mode="lines",
253+
# name="Baseline (No Deviation)",
254+
# line=attr(width=2, color="blue")
255+
# )
256+
# load_trace2 = PlotlyJS.scatter(
257+
# x=collect(plot_hours),
258+
# y=inputs_mc.loads_kw_by_scenario[mc_scenario_id][plot_hours],
259+
# mode="lines",
260+
# name="Discrete Monte Carlo Sample",
261+
# line=attr(width=2, color="red", dash="dash")
262+
# )
263+
#
264+
# load_layout = PlotlyJS.Layout(
265+
# title="Load Profile Comparison (First Week)",
266+
# xaxis_title="Hour",
267+
# yaxis_title="Load (kW)",
268+
# hovermode="x unified"
269+
# )
270+
#
271+
# load_plot = PlotlyJS.plot([load_trace1, load_trace2], load_layout)
272+
#
273+
# # Create PV production comparison plot
274+
# pv_trace1 = PlotlyJS.scatter(
275+
# x=collect(plot_hours),
276+
# y=inputs_discrete.production_factor_by_scenario[baseline_scenario_id]["PV"][plot_hours],
277+
# mode="lines",
278+
# name="Baseline (No Deviation)",
279+
# line=attr(width=2, color="blue")
280+
# )
281+
# pv_trace2 = PlotlyJS.scatter(
282+
# x=collect(plot_hours),
283+
# y=inputs_mc.production_factor_by_scenario[mc_scenario_id]["PV"][plot_hours],
284+
# mode="lines",
285+
# name="Discrete Monte Carlo Sample",
286+
# line=attr(width=2, color="red", dash="dash")
287+
# )
288+
#
289+
# pv_layout = PlotlyJS.Layout(
290+
# title="PV Production Profile Comparison (First Week)",
291+
# xaxis_title="Hour",
292+
# yaxis_title="Production Factor",
293+
# hovermode="x unified"
294+
# )
295+
#
296+
# pv_plot = PlotlyJS.plot([pv_trace1, pv_trace2], pv_layout)
297+
#
298+
# # Save plots
299+
# PlotlyJS.savefig(load_plot, "load_comparison.html")
300+
# PlotlyJS.savefig(pv_plot, "pv_comparison.html")
301+
# println(" └─ Plots saved to load_comparison.html and pv_comparison.html")
302+
end
303+
304+
# Restore logger
305+
Logging.global_logger(original_logger)
306+
307+
println("\n" * "="^80)
308+
println("✅ Tests Complete")
309+
println("="^80)

0 commit comments

Comments
 (0)