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Merged
tsmbland merged 1 commit into from
Sep 11, 2026
Merged

Revert "Use TimeSliceSelection for demand maps" #1546

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7 changes: 4 additions & 3 deletions benches/assets.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ use muse2::model::Model;
use muse2::output::DataWriter;
use muse2::process::{Process, ProcessID};
use muse2::simulation::candidate_assets_for_next_year;
use muse2::simulation::investment::{collect_preset_demands_for_year, select_best_assets};
use muse2::simulation::investment::{flatten_preset_demands_for_year, select_best_assets};
use muse2::simulation::market::{
collect_agent_limits, get_asset_options, get_demand_portion_for_market, get_responsible_agents,
};
Expand Down Expand Up @@ -154,11 +154,12 @@ fn criterion_benchmark(c: &mut Criterion) {
.next()
.expect("No agent found responsible for the target commodity/region/year");

let net_demand = collect_preset_demands_for_year(&model.commodities, YEAR);
let net_demand =
flatten_preset_demands_for_year(&model.commodities, &model.time_slice_info, YEAR);
let demand = get_demand_portion_for_market(
&model.time_slice_info,
&net_demand,
commodity,
&commodity.id,
region_id,
commodity_portion,
);
Expand Down
4 changes: 2 additions & 2 deletions src/fixture.rs
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Expand Up @@ -19,7 +19,7 @@ use crate::simulation::investment::appraisal::LCOXMetric;
use crate::simulation::investment::appraisal::{
AppraisalOutput, coefficients::ObjectiveCoefficients,
};
use crate::time_slice::{TimeSliceID, TimeSliceInfo, TimeSliceLevel, TimeSliceSelection};
use crate::time_slice::{TimeSliceID, TimeSliceInfo, TimeSliceLevel};
use crate::units::{
Activity, ActivityPerCapacity, Capacity, Dimensionless, Flow, MoneyPerActivity,
MoneyPerCapacity, MoneyPerCapacityPerYear, Year,
Expand Down Expand Up @@ -406,7 +406,7 @@ pub fn appraisal_output(asset: Asset, time_slice: TimeSliceID) -> AppraisalOutpu
let activity_coefficients = indexmap! { time_slice.clone() => MoneyPerActivity(0.5) };
let market_costs = indexmap! { time_slice.clone() => MoneyPerActivity(0.4) };
let activity = indexmap! { time_slice.clone() => Activity(10.0) };
let unmet_demand = indexmap! { TimeSliceSelection::Single(time_slice.clone()) => Flow(5.0) };
let unmet_demand = indexmap! { time_slice.clone() => Flow(5.0) };
AppraisalOutput {
asset: AssetRef::from(asset),
coefficients: Arc::new(ObjectiveCoefficients {
Expand Down
36 changes: 13 additions & 23 deletions src/output.rs
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Expand Up @@ -7,7 +7,7 @@ use crate::region::RegionID;
use crate::simulation::investment::appraisal::AppraisalOutput;
use crate::simulation::optimisation::{FlowMap, Solution};
use crate::simulation::prices::PriceMap;
use crate::time_slice::{TimeSliceID, TimeSliceLevel, TimeSliceSelection};
use crate::time_slice::TimeSliceID;
use crate::units::{Activity, Capacity, Flow, Money, MoneyPerActivity, MoneyPerFlow};
use anyhow::{Context, Result, ensure};
use csv;
Expand Down Expand Up @@ -257,9 +257,8 @@ struct AppraisalResultsTimeSliceRow {
time_slice: TimeSliceID,
activity: Activity,
activity_coefficient: MoneyPerActivity,
time_slice_level: TimeSliceLevel,
demand_for_selection: Flow,
unmet_demand_for_selection: Flow,
demand: Flow,
unmet_demand: Flow,
}

/// For writing extra debug information about the model
Expand Down Expand Up @@ -503,17 +502,13 @@ impl DebugDataWriter {
milestone_year: u32,
run_description: &str,
appraisal_results: &[AppraisalOutput],
demand: &IndexMap<TimeSliceSelection, Flow>,
time_slice_level: TimeSliceLevel,
demand: &IndexMap<TimeSliceID, Flow>,
) -> Result<()> {
for result in appraisal_results {
for (time_slice, activity) in &result.activity {
let activity_coefficient = result.coefficients.activity_coefficients[time_slice];
// Map the individual time slice back to its containing selection so we can look
// up selection-level demand and unmet demand.
let selection = time_slice_level.containing_selection(time_slice);
let demand = demand[&selection];
let unmet_demand = result.unmet_demand[&selection];
let demand = demand[time_slice];
let unmet_demand = result.unmet_demand[time_slice];
let row = AppraisalResultsTimeSliceRow {
milestone_year,
run_description: self.with_context(run_description),
Expand All @@ -523,9 +518,8 @@ impl DebugDataWriter {
time_slice: time_slice.clone(),
activity: *activity,
activity_coefficient,
time_slice_level,
demand_for_selection: demand,
unmet_demand_for_selection: unmet_demand,
demand,
unmet_demand,
};
self.appraisal_results_time_slice_writer.serialize(row)?;
}
Expand Down Expand Up @@ -610,8 +604,7 @@ impl DataWriter {
milestone_year: u32,
run_description: &str,
appraisal_results: &[AppraisalOutput],
demand: &IndexMap<TimeSliceSelection, Flow>,
time_slice_level: TimeSliceLevel,
demand: &IndexMap<TimeSliceID, Flow>,
) -> Result<()> {
if let Some(wtr) = &mut self.debug {
wtr.write_appraisal_results(milestone_year, run_description, appraisal_results)?;
Expand All @@ -620,7 +613,6 @@ impl DataWriter {
run_description,
appraisal_results,
demand,
time_slice_level,
)?;
}

Expand Down Expand Up @@ -725,7 +717,7 @@ mod tests {
appraisal_output, asset, assets, commodity_id, multi_tranche_asset, region_id, time_slice,
};
use crate::simulation::investment::appraisal::AppraisalOutput;
use crate::time_slice::{TimeSliceID, TimeSliceLevel, TimeSliceSelection};
use crate::time_slice::TimeSliceID;
use indexmap::indexmap;
use itertools::{Itertools, assert_equal};
use rstest::rstest;
Expand Down Expand Up @@ -1135,7 +1127,7 @@ mod tests {
let milestone_year = 2020;
let run_description = "test_run".to_string();
let dir = tempdir().unwrap();
let demand = indexmap! {TimeSliceSelection::Single(time_slice.clone()) => Flow(100.0) };
let demand = indexmap! {time_slice.clone() => Flow(100.0) };

// Write appraisal time slice results
{
Expand All @@ -1146,7 +1138,6 @@ mod tests {
&run_description,
&[appraisal_output],
&demand,
TimeSliceLevel::DayNight,
)
.unwrap();
writer.flush().unwrap();
Expand All @@ -1162,9 +1153,8 @@ mod tests {
time_slice: time_slice.clone(),
activity: Activity(10.0),
activity_coefficient: MoneyPerActivity(0.5),
time_slice_level: TimeSliceLevel::DayNight,
demand_for_selection: Flow(100.0),
unmet_demand_for_selection: Flow(5.0),
demand: Flow(100.0),
unmet_demand: Flow(5.0),
};
let records: Vec<AppraisalResultsTimeSliceRow> =
csv::Reader::from_path(dir.path().join(APPRAISAL_RESULTS_TIME_SLICE_FILE_NAME))
Expand Down
87 changes: 52 additions & 35 deletions src/simulation/investment.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -8,9 +8,9 @@ use crate::output::DataWriter;
use crate::process::ProcessID;
use crate::region::RegionID;
use crate::simulation::prices::Prices;
use crate::time_slice::{TimeSliceInfo, TimeSliceLevel, TimeSliceSelection};
use crate::time_slice::{TimeSliceID, TimeSliceInfo, TimeSliceLevel, TimeSliceSelection};
use crate::timeit::InvestmentTimer;
use crate::units::{ActivityPerCapacity, Capacity, Flow, FlowPerCapacity};
use crate::units::{ActivityPerCapacity, Capacity, Dimensionless, Flow, FlowPerCapacity};
use anyhow::{Result, ensure};
use context_manager;
use indexmap::IndexMap;
Expand All @@ -27,11 +27,11 @@ use appraisal::{
sort_and_filter_appraisal_outputs,
};

/// A map of demand across time-slice selections for a specific market
pub type DemandMap = IndexMap<TimeSliceSelection, Flow>;
/// A map of demand across time slices for a specific market
pub type DemandMap = IndexMap<TimeSliceID, Flow>;

/// Demand for a given combination of commodity, region and time-slice selection
pub type AllDemandMap = IndexMap<(CommodityID, RegionID, TimeSliceSelection), Flow>;
/// Demand for a given combination of commodity, region and time slice
pub type AllDemandMap = IndexMap<(CommodityID, RegionID, TimeSliceID), Flow>;

/// Perform agent investment to determine capacity investment of new assets for next milestone year.
///
Expand All @@ -56,7 +56,8 @@ pub fn perform_agent_investment(
writer: &mut DataWriter,
) -> Result<Vec<AssetRef>> {
// Initialise net demand map
let mut net_demand = collect_preset_demands_for_year(&model.commodities, year);
let mut net_demand =
flatten_preset_demands_for_year(&model.commodities, &model.time_slice_info, year);

// Keep a list of all the assets selected
// This includes Commissioned assets that are selected for retention, and new Ready assets
Expand Down Expand Up @@ -127,26 +128,39 @@ pub fn perform_agent_investment(
Ok(all_selected_assets)
}

/// Collect the preset commodity demands for a given year into a map of commodity, region and
/// time slice selection to demand.
/// Flatten the preset commodity demands for a given year into a map of commodity, region and
/// time slice to demand.
///
/// Demand for each commodity is stored at its natural time-slice selection level, matching the
/// balance level at which the investment appraisal operates.
pub fn collect_preset_demands_for_year(commodities: &CommodityMap, year: u32) -> AllDemandMap {
/// Since demands for some commodities may be specified at a coarser time slice level, we need to
/// distribute these demands over all time slices. Note: the way that we do this distribution is
/// irrelevant, as demands will only be balanced to the appropriate level, but we still need to do
/// this for the solver to work.
///
/// **TODO**: these assumptions may need to be revisited, e.g. when we come to storage technologies
pub fn flatten_preset_demands_for_year(
commodities: &CommodityMap,
time_slice_info: &TimeSliceInfo,
year: u32,
) -> AllDemandMap {
let mut demand_map = AllDemandMap::new();
for (commodity_id, commodity) in commodities {
for ((region_id, data_year, time_slice_selection), demand) in &commodity.demand {
if *data_year != year {
continue;
}
demand_map.insert(
(
commodity_id.clone(),
region_id.clone(),
time_slice_selection.clone(),
),
*demand,
);

// We split the demand equally over all time slices in the selection
// NOTE: since demands will only be balanced to the time slice level of the commodity
// it doesn't matter how we do this distribution, only the total matters.
#[allow(clippy::cast_precision_loss)]
let n_time_slices = time_slice_selection.iter(time_slice_info).count() as f64;
let demand_per_slice = *demand / Dimensionless(n_time_slices);
for (time_slice, _) in time_slice_selection.iter(time_slice_info) {
demand_map.insert(
(commodity_id.clone(), region_id.clone(), time_slice.clone()),
demand_per_slice,
);
}
}
}
demand_map
Expand All @@ -164,20 +178,19 @@ pub fn collect_preset_demands_for_year(commodities: &CommodityMap, year: u32) ->
pub fn update_net_demand_map(demand: &mut AllDemandMap, flows: &FlowMap, assets: &[AssetRef]) {
for ((asset, commodity_id, time_slice), flow) in flows {
if assets.contains(asset) {
let key = (
commodity_id.clone(),
asset.region_id().clone(),
time_slice.clone(),
);

// Only consider input flows and output flows from the primary output commodity
// (excluding secondary outputs)
if (flow < &Flow(0.0))
|| asset
.primary_output()
.is_some_and(|p| &p.commodity.id == commodity_id)
{
let level = asset
.get_flow(commodity_id)
.unwrap()
.commodity
.time_slice_level;
let selection = level.containing_selection(time_slice);
let key = (commodity_id.clone(), asset.region_id().clone(), selection);
// Note: we use the negative of the flow as input flows are negative in the flow map.
demand
.entry(key)
Expand Down Expand Up @@ -281,7 +294,12 @@ pub fn get_demand_limiting_capacity(
> ActivityPerCapacity(0.0)
})
})
.map(|(bucket, _)| demand[&bucket])
.map(|(bucket, _)| {
bucket
.iter(time_slice_info)
.map(|(ts, _)| demand[ts])
.sum::<Flow>()
})
.sum()
});

Expand Down Expand Up @@ -418,7 +436,6 @@ pub fn select_best_assets(
&format!("{} {} round {}", commodity.id, agent.id, round),
&outputs,
&demand,
commodity.time_slice_level,
)?;

// Sort by investment priority and discard non-feasible options
Expand Down Expand Up @@ -645,7 +662,7 @@ mod tests {
let asset = asset(process);

// Create demand map - demand of 10.0 for our time slice
let demand = indexmap! { TimeSliceSelection::Single(time_slice.clone()) => Flow(10.0)};
let demand = indexmap! { time_slice.clone() => Flow(10.0)};

// Call the function
let result = get_demand_limiting_capacity(&time_slice_info, &asset, &commodity_rc, &demand);
Expand Down Expand Up @@ -689,8 +706,8 @@ mod tests {

// Create demand map with different demands for each time slice
let demand = indexmap! {
TimeSliceSelection::Single(time_slice1.clone()) => Flow(4.0), // Requires capacity of 4.0/0.2 = 20.0
TimeSliceSelection::Single(time_slice2.clone()) => Flow(3.0), // Would require infinite capacity, but should be skipped
time_slice1.clone() => Flow(4.0), // Requires capacity of 4.0/0.2 = 20.0
time_slice2.clone() => Flow(3.0), // Would require infinite capacity, but should be skipped
};

// Call the function
Expand Down Expand Up @@ -756,8 +773,8 @@ mod tests {
let asset = asset(process);

let demand = indexmap! {
TimeSliceSelection::Single(time_slice1.clone()) => Flow(5.0),
TimeSliceSelection::Single(time_slice2.clone()) => Flow(5.0),
time_slice1.clone() => Flow(5.0),
time_slice2.clone() => Flow(5.0),
};

let result =
Expand Down Expand Up @@ -797,7 +814,7 @@ mod tests {
process.activity_limits = process_activity_limits_map(process.regions.clone(), limits);

let asset = asset(process);
let demand = indexmap! { TimeSliceSelection::Single(time_slice)=> demand_value };
let demand = indexmap! { time_slice => demand_value };
assert_eq!(
calculate_candidate_asset_capacity_scale(&asset, &commodity_rc, &demand),
expected
Expand Down
14 changes: 8 additions & 6 deletions src/simulation/investment/appraisal/constraints.rs
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Expand Up @@ -4,6 +4,7 @@ use super::optimisation::Variable;
use crate::asset::AssetRef;
use crate::commodity::Commodity;
use crate::time_slice::{TimeSliceID, TimeSliceInfo};
use crate::units::Flow;
use highs::RowProblem as Problem;
use indexmap::IndexMap;

Expand Down Expand Up @@ -48,12 +49,13 @@ pub fn add_demand_constraints(
activity_vars: &IndexMap<TimeSliceID, Variable>,
) {
for ts_selection in time_slice_info.iter_selections_at_level(commodity.time_slice_level) {
let demand_for_ts_selection = demand[&ts_selection];
let flow_coeff = asset.get_flow(&commodity.id).unwrap().coeff;
let terms: Vec<_> = ts_selection
.iter(time_slice_info)
.map(|(time_slice, _)| (activity_vars[time_slice], flow_coeff.value()))
.collect();
let mut demand_for_ts_selection = Flow(0.0);
let mut terms = Vec::new();
for (time_slice, _) in ts_selection.iter(time_slice_info) {
demand_for_ts_selection += demand[time_slice];
let flow_coeff = asset.get_flow(&commodity.id).unwrap().coeff;
terms.push((activity_vars[time_slice], flow_coeff.value()));
}
problem.add_row(0.0..=demand_for_ts_selection.value(), terms);
}
}
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