Model outputs

After every successful solve, the HiGHS solution is handed back to FlexTool in-memory together with the polars FlexData bundle that produced the LP. flextool.process_outputs.write_outputs then turns that pair into the result tables documented on this page.

The result tables are stored as parquet (the canonical store) and a configurable mix of derived artefacts:

Location	Format	When written	Notes
output_parquet/<scenario>/*.parquet	parquet	always (canonical)	one file per result table; read by the GUI, the scenario-comparison tools, and read_parquet_dir=True re-runs
output_csv/<scenario>/*.csv	CSV	when output-csv is enabled	one CSV per parquet, plus summary_solve.csv (diagnostic overview)
output_excel/output_<scenario>.xlsx	Excel	when output-excel is enabled	one workbook per scenario; alphabetically ordered sheets
output_plots/<scenario>/*.png (or svg/pdf)	image	when output-plot is enabled	driven by templates/default_plots.yaml
<output-location>/results.sqlite (or --results-db-url)	SpineDB	when output-spinedb is enabled (--write-methods spinedb)	the processed result tables written into a SpineDB using the FlexTool results schema (flextool/schemas/spinedb_results_schema.json); one Spine alternative per run (named after the scenario) so multiple runs coexist in one file. Time series land as nested Spine Map values on the model entity classes (node / unit / connection / group / model).

The CSV / Excel / plot files are derived from the parquets, so deleting or regenerating them is safe. summary_solve.csv is a diagnostic file aimed at a quick overview of a solve — it lists objective decomposition, period weighting, CO2 total, and any non-zero slack uses.

All annualized numbers are scaled to a full year of operation by dividing through by complete_period_share_of_year. All values are returned in user units — auto-scaling (see LP scaling pipeline) and time-block aggregation under flex-temporal decomposition are unwound before they reach the parquets, so users always read MW / MWh / [CUR] in the original problem space.

For stochastic models the parquets carry only the realised branch of each solve by default. Setting the model parameter output_horizon = yes adds the forecast-branch rows to the time-series tables (*_t, *_dt) so the unrealised futures are visible for debugging — note that cost aggregates in that mode mix realised + unrealised contributions and should not be used as final results (see also How to use stochastics).

• Costs
• Cost by entity
• Prices
• Energy flows
• Capacity factors
• Energy balance in nodes
• Unit online and startup
• Unit curtailment and VRE potential
• Node group results
• Flow group results
• Capacity and investment results
• CO2 emissions
• Reserves
• Inertia and non-synchronous generation
• Ramps
• DC power flow
• Slack and penalty values

Costs

• model entity cost_annualized parameter — M[CUR] (millions of user-chosen currency); annualized total cost broken down by category:
  • unit investment & retirement — M[CUR] cost of investing in unit capacity or salvage from retiring it
  • connection investment & retirement — M[CUR] same for connections
  • storage investment & retirement — M[CUR] same for node states (storages)
  • fixed cost pre-existing — M[CUR] fixed O&M for pre-existing capacity
  • fixed cost invested — M[CUR] fixed O&M for newly invested capacity
  • fixed cost reduction of divestments — M[CUR] fixed O&M removed by divestments
  • commodity cost / commodity sales — M[CUR] cost of buying / revenue from selling commodities
  • co2 — M[CUR] cost of CO2 emissions caused by commodities with CO2 content
  • other operational — M[CUR] other variable O&M costs
  • starts — M[CUR] start-up costs
  • upward / downward slack penalty — M[CUR] cost of involuntary demand reduction / increase
  • inertia slack penalty — M[CUR] cost of not meeting the inertia constraint
  • non-synchronous slack penalty — M[CUR] cost of not meeting the non-synchronous constraint
  • capacity margin penalty — M[CUR] cost of not meeting the capacity margin constraint
  • upward / downward reserve slack penalty — M[CUR] cost of not meeting the reserve constraint
• model entity cost_t parameter — M[CUR] same categories as above but per timestep (no investment / fixed terms)
• model entity cost_discounted_solve parameter — M[CUR] costs for the solve considering discounting and years represented (NPV currency)
• model entity cost_discounted_total parameter — M[CUR] same, totalled over all realised periods

Cost by entity

The system Costs above are also broken down per entity at the period level, so you can see which unit, connection or node drives each cost category. Six result tables are produced — one for each entity type (unit, connection, node) in each of two flavours:

• annualized — M[CUR/a], scaled to a full year of operation (the same basis as cost_annualized)
• discounted — M[CUR], net present value over the horizon (the same basis as cost_discounted_total)

Each table is indexed by (period, entity) — the entity level is named after its type (unit, connection or node) — and carries one column per cost category:

• unit entity cost_annualized / cost_discounted parameters — per-unit cost break-down
• connection entity cost_annualized / cost_discounted parameters — per-connection cost break-down
• node entity cost_annualized / cost_discounted parameters — per-node cost break-down

The categories mirror the system Costs decomposition, restricted to those that apply to the entity type:

• commodity cost / commodity sales — M[CUR] cost of buying / revenue from selling commodities
• co2 — M[CUR] cost of CO2 emissions caused by the entity's flows
• other operational — M[CUR] other variable O&M costs
• starts — M[CUR] start-up costs (units)
• investment / retirement — M[CUR] cost of investing in unit / connection capacity or salvage from retiring it (named storage investment / storage retirement in the node table, where the capacity is node-state storage energy)
• fixed cost pre-existing / fixed cost invested / fixed cost reduction of divestments — M[CUR] fixed O&M for pre-existing, newly invested and divested capacity
• upward slack penalty / downward slack penalty (node table only) — M[CUR] cost of involuntary demand reduction / increase at the node

Attribution rules (matching the LP objective):

• Fuel / commodity cost is attributed to the consuming process — the unit or connection that draws the commodity — not to the source node.
• CO2 cost for a process is the sum over every priced group the process touches, so a flow that contributes emissions to several priced groups carries the full cost of each.

By construction these tables are purely additive collapses of the same per-entity intermediates the system summary uses, so summing any category over all entities reproduces the system summary: the unit + connection + node contributions for a given category and period add up to the matching cost_annualized / cost_discounted_total category value (annualized_costs_d_p / costs_discounted_d_p in the parquet store).

These are period-level tables only — there are no per-timestep per-entity cost tables.

Prices

• node entity price_t parameter — [CUR/MWh] dual of the balance constraint for every node that maintains an energy balance (nominal currency at period d, un-discounted from NPV)
• group__reserve__upDown entity reserve_price_t parameter — [CUR/MWh] dual of the reserve balance constraint
• group entity co2_price_period parameter — [CUR/tCO2] shadow price of the per-period CO2 cap (nominal currency, sign-flipped so a binding cap shows positive)
• group entity co2_price_total parameter — [CUR/tCO2] shadow price of the cumulative CO2 cap (NPV currency, broadcast across periods)

Energy flows

• unit__node entity flow_annualized parameter — [MWh] cumulative flow from the node (if node is input) or to the node (if node is output), annualized to a full year
• unit__node entity flow_t parameter — [MW] flow at each timestep
• connection__node__node entity flow_annualized parameter — [MWh] annualized cumulative flow through the connection (left-to-right is positive)
• connection__node__node entity flow_t parameter — [MW] flow through the connection at each timestep
• connection__node__node entity connection_losses_annualized parameter — [MWh] annualized losses on the connection
• connection__node__node entity connection_losses_t parameter — [MW] losses on the connection at each timestep

Optional output: output_connection_flows_separate

• connection__node__node entity flow_to_first_node_annualized parameter — [MWh] annualized cumulative flow only to the left (first) node
• connection__node__node entity flow_to_second_node_annualized parameter — [MWh] annualized cumulative flow only to the right (second) node
• connection__node__node entity flow_to_first_node_t parameter — [MW] flow to the left (first) node at each timestep
• connection__node__node entity flow_to_second_node_t parameter — [MW] flow to the right (second) node at each timestep

Capacity factors

• unit__node entity cf parameter — [per unit] average capacity factor of the flow: time-average of flow [MWh/h] divided by capacity [MW] of the unit input or output. One table per direction (unit_cf__inputNode, unit_cf__outputNode).
• connection entity cf parameter — [per unit] average capacity factor of the absolute flow (flows in either direction count as utilization), divided by connection capacity

Energy balance in nodes

• node entity balance parameter — [MWh] annualized period sum of all balance contributions: From units, From connections, To units, To connections, Self discharge, Loss of load (upward slack), Excess load (downward slack), Inflow
• node entity balance_t parameter — [MW] same categories per timestep
• node entity state_t parameter — [MWh] storage state of the node at each timestep (state nodes only; multiplied by entity_unitsize so the value is in storage MWh, not the scaled LP variable)

Unit online and startup

• unit entity online_t parameter — [count] number of units online at each timestep
• unit entity online_average parameter — [count] average online status over the period (weighted by step duration)
• unit entity startup_annualized parameter — [count] startups during the period scaled to a full year (weighted by rp_cost_weight to handle representative periods correctly)

Unit curtailment and VRE potential

For VRE units (units that use an upper_limit profile) the model also reports curtailment and potential generation against each output node:

• unit__node entity VRE_potential_t parameter — [MW] potential VRE generation at the timestep (capacity × profile)
• unit__node entity VRE_potential_annualized parameter — [MWh] annualized potential VRE generation in the period
• unit__node entity curtailment_t parameter — [MW] potential minus actual generation at the timestep
• unit__node entity curtailment_annualized parameter — [MWh] annualized curtailed energy in the period
• unit__node entity curtailment_share_t parameter — [0–1] timestep curtailment / potential
• unit__node entity curtailment_share parameter — [0–1] period curtailment / period potential

Node group results

group entities with print_indicators = yes produce indicator tables aggregating flows / inflows / slacks over their member nodes:

• group entity indicator (period table) — gives a set of indicators for all node members:
  • Loss of load share — [0–1] upward slack relative to inflow
  • VRE share of demand — [0–1] share of inflow served by VRE sources
  • Excess load share — [0–1] downward slack relative to inflow
  • Curtailed VRE of demand — [0–1] curtailed VRE relative to inflow
  • Annualized inflow — [MWh] sum of inflow to member nodes scaled to a year
  • Curtailed VRE of potential VRE — [0–1] curtailed share of potential VRE
• group entity indicator_t (timestep table) — exposes the timestep-level building blocks of the above: Loss of load [MWh/step], VRE generation [MWh/step], Excess load [MWh/step], Curtailed VRE [MWh/step], Timestep inflow [MWh/step], Curtailed VRE of potential VRE [0–1], Annualized inflow [MWh], VRE share of demand [0–1]
• group entity VRE_share_t parameter — [0–1] share of inflow served by VRE sources at each timestep (one column per node group)
• group entity VRE_share parameter — [0–1] period-average VRE share
• group entity total_inflow_annualized parameter — [MWh] annualized sum of inflow to member nodes
• group entity total_inflow_t parameter — [MWh/step] timestep inflow to member nodes

group entities with print_dispatch = yes produce a multi-column dispatch table over the group:

• group entity flows_t parameter — [MWh/step] dispatch decomposed by (type, item) where type is one of: slack (upward/downward), from_unit, from_unitGroup, to_unit, to_unitGroup, from_connection, from_connectionGroup, to_connection, to_connectionGroup, inflow, internal_losses (units/connections/storages)
• group entity flows_annualized parameter — [MWh] same decomposition, annualized period totals

Flow group results

flowGroup entities whose flow_aggregator is standalone_aggregator_only or both produce aggregate flow statistics over their member (process, node) legs (listed via flowGroup__unit__node / flowGroup__connection__node):

• group_flow__d.csv — per-(flowGroup, period) totals:
  • cumulative_flow parameter — [MWh] sum of |flow| over the period for all member legs
  • average_flow parameter — [MW] average power equivalent (cumulative_flow / period hours)
• group_flow__dt.csv — per-(flowGroup, period, time) signed net flow [MW] (sign convention: flow into the group's nodes is positive, flow out of them is negative). Intended for spreadsheet post-processing; because the values are signed they do not stack, so this series is not meant for stacked dispatch plots.

(For member flows shown as aggregated bands inside a node group's dispatch table, set flow_aggregator to dispatch_plots_only or both instead — see Node group results and the reference.)

Capacity and investment results

• unit, connection, node entities capacity parameter — [MW or MWh] decomposed into:

  • existing — capacity assumed at the start of the period
  • invested — capacity the model decided to invest in for the period
  • divested — capacity the model decided to retire at the start of the period
  • total — existing + invested − divested

  Period axis covers every period in which an investment or a divestment decision occurs (both the invest-eligible and divest-eligible periods feed into d_realize_invest), so divest-only periods are visible even when no new capacity is added. - unit, connection, node entities invest_marginal parameter — [CUR/MW or MWh] effective dual of the investment decision: zero means the model is at the unconstrained optimum; positive means an active upper bound (per-entity, per-group, period, total or cumulative caps are summed automatically); negative is not expected and indicates a numerical artefact worth investigating

CO2 emissions

• model entity CO2 parameter — [Mt] horizon-total CO2 across all groups and units (uses years_represented_d to convert annualized emissions to horizon totals)
• group entity CO2_annualized parameter — [Mt/yr] annualized CO2 emissions caused (or removed) by units and connections in the group
• unit__source__sink entity CO2_annualized parameter — [Mt/yr] annualized CO2 emissions per flow leg

Reserves

• unit__reserve__upDown__node and connection__reserve__upDown__node reservation_t parameter — [MW] reserve provision at each timestep
• unit__reserve__upDown__node and connection__reserve__upDown__node reservation_average parameter — [MW] period-average reserve provision

Inertia and non-synchronous generation

• group entity inertia_t parameter — [MWs] total inertia in the group of nodes at each timestep
• group entity inertia_largest_flow_t parameter — [MW] largest individual flow coming into the group of nodes with has_inertia
• group entity inertia_unit_node_t parameter — [MWs] inertia contribution per (unit, node) at each timestep, one column per contributing (unit, node)

Ramps

• unit__node entity ramp_t parameter — [MW] ramp of the unit input or output flow at each timestep (one table per direction: unit_ramp__inputNode__dt, unit_ramp__outputNode__dt)

The "ramp room" envelope on the node entity (additional headroom for upward / downward ramps from non-VRE units, VRE units, and connections) is currently not emitted by the new pipeline — it is on the roadmap for re-exposure once the corresponding post-processing is ported.

DC power flow

When the network contains DC-power-flow connections / nodes (is_dc_power_flow = yes):

• node entity dc_angle_t parameter — [rad] voltage angle at each timestep
• connection entity dc_angle_diff_t parameter — [rad] angle difference across the connection at each timestep

Slack and penalty values

Slack uses are listed in the Energy balance in nodes section (Loss of load, Excess load and per-node slack_up / slack_down parameters with _t and annualized variants) and aggregated in Costs. Group-level slack tables:

• group entity slack_capacity_margin parameter — [MW] capacity-margin shortfall in investment periods
• group entity slack_inertia_t / slack_inertia — [MWs] inertia shortfall, timestep / annualized
• group entity slack_nonsync_t / slack_nonsync — [MW] non-synchronous-share shortfall, timestep / annualized
• group__reserve__upDown entity slack_reserve_t / slack_reserve — [MW] reserve shortfall, timestep / annualized

For the sign / direction conventions of slacks, see Slack convention. For how block-aware solves (flex-temporal decomposition) and spatial Benders decomposition feed into these tables, see Decomposition — the result writers always operate on the fine-timeline, full-spatial solution, so the parquet tables look identical to a monolithic run.