Scenario catalog

Every payload in internal/scenarios/testdata/scenarios/ is a self-contained Job that must validate + emit cleanly for its target; TestScenarios asserts the emitted tree contains the expected framework-native markers, and TestScenariosCapabilityGating asserts each target-specific mode is rejected on the targets that can't honor it. All outputs are shaped to be directly loadable by the pinned framework (see Targets and versions), so "emits cleanly" means "runnable".

PyPSA

Scenario	Exercises	Key emitted markers
pypsa_generators	supply generators, capex/opex	p_nom_min, capital_cost, marginal_cost
pypsa_conversion	multi-output CHP Link	bus2, efficiency2
pypsa_storage	StorageUnit detail	efficiency_store, standing_loss, cyclic_state_of_charge
pypsa_transmission	inter-node Link	n1::electricity, n2::electricity
pypsa_trade	import/export market gens	market_import, market_export
pypsa_committable	unit commitment	committable, start_up_cost, ramp_limit_up
pypsa_emission	CO₂ cap GlobalConstraint	primary_energy, co2_emissions
pypsa_constraints	linear-constraint IR	max_pv
pypsa_extensions	must-run dispatch (operation.equals_pu pins both bounds), a system-wide capacity cap (capacity.systemwide_max), an import limited by a series (trade.import.limit)	p_min_pu = p_max_pu, p_nom_max, backup_import
pypsa_node_override	one tech on [n1,n2] with per-node capacity/cost overrides	pv@n1, pv@n2, per-node p_nom_max
pypsa_physics_precomputed	physics pv model carried by a precomputed CF series	_performance.json (type: physics, precomputed)
pypsa_operate	rolling-horizon dispatch over fixed capacities (optimize_with_rolling_horizon)	gen, fixed p_nom
pypsa_alternatives	MGA near-optimal alternative (optimize_mga; the payload still uses the deprecated solver.options.mga namespace and validates with a warning)	p_nom_extendable
pypsa_stochastic	two-stage stochastic (set_scenarios + per-scenario overrides)	_scenarios.json (fuel_high, translated marginal_cost)
pypsa_constraints_sidecar	tech-scoped constraint applied by run.py as a linopy constraint (binding, objective-checked in E2E)	force_pv in _constraints.json
pypsa_trade_price_series	time-varying import price (objective-checked in E2E)	market_import, generators-marginal_cost.csv
pypsa_power_flow	mode power_flow: an HV slack feeding two MV nodes through a transformer and a line, PV with a dispatch series, two loads (converges 4/4 in E2E; the linear method and a diverging load are E2E-checked too)	v_nom, lines.csv, transformers.csv, control: Slack, generators-p_set.csv
pypsa_lopf	plan under the voltage law (network.model: linear_power_flow): a 110 kV ring, an expandable priced line with max_loading; E2E checks Kirchhoff's law around the loop and the security-constrained variant	s_nom_extendable, s_max_pu, capital_cost on lines.csv
pypsa_store	energy-sized storage as a Store with charger/discharger and the _storage.json constraints; a pumped unit with a pinned soc_profile	stores.csv, battery_charge, equal_power, state_of_charge_set
pypsa_modular	integer modules on a generator and a store	p_nom_mod, e_nom_mod
pypsa_retire_slack	continuous retirement of an old unit; priced slack on an island short of capacity	::unmet, retirable old
pypsa_copperplate	copperplate: a free hub joins the nodes, the arc is dropped	copperplate::electricity
pypsa_uc_extras	start/stop ramps, standby cost, prior uptime on a committable unit	ramp_limit_start_up, stand_by_cost, up_time_before
pypsa_quadratic	quadratic variable cost (QP on HiGHS)	marginal_cost_quadratic
pypsa_multi_period	two investment periods: a 2020 unit retires before 2040, new capacity is built per period	investment_periods.csv, new-2030, new-2040, build_year
pypsa_power_flow_reactive	reactive power and voltage control: slack set-point 1.02 pu, a pv generator holding 1.0 pu, loads with a power factor and a reactive series, a shunt, a per-length line impedance, a phase-shifting transformer, a controlled HVDC link into an island PyPSA balances itself (E2E pins the held voltages)	v_mag_pu_set, control: PV, q_set, shunt_impedances.csv, phase_shift, links p_set

Calliope 0.7 (calliope)

Scenario	Exercises	Key emitted markers
calliope_plan	base plan run	mode: base, base_tech: supply, solver: cbc
calliope_spores	SPORES / MGA	mode: spores, number:
calliope_operate	receding-horizon dispatch	mode: operate
calliope_custommath	constraint IR + emission limit → additional_math.yaml	extra_math, max_pv, cost_flow_in
calliope_storage	storage tech	base_tech: storage, storage_cap_max, cost_storage_cap
calliope_source_native	first-class technology.source + area (source_use_max, source_unit, area_use_max)	source_use_max, area_use_max
calliope_conversion	multi-output CHP → carrier lists + ratio-pinning extra math (rigid ratios, single-counted fuel)	meme_ratio_chp_out_heat, masked balance_conversion
calliope_transmission	transmission tech across two nodes	base_tech: transmission, link_from, link_to
calliope_node_override	per-node cost_flow_cap override under nodes.<n>.techs	cost_flow_cap, overridden value
calliope_trade	import/export market techs (import = supply at price + co2 factor; export = flexible demand paid the price)	market_import, market_export, cost_flow_in: -30
calliope_committable	MILP unit commitment (integer units, min stable load)	cap_method: integer, flow_cap_per_unit, extra_math: [milp]
calliope_resample	time_aggregation: resample → config.init.resample.timesteps	resample:, timesteps: 3h
calliope_piecewise	piecewise part-load curve → piecewise_constraints math on a fixed-capacity conversion	piecewise_x, breakpoints
calliope_multi_node	one demand tech at two nodes → one data table with a column per node (#7); solves to the same objective on PyPSA and both Calliope releases (TestE2EMultiNodeDemandConsistency)	techs,load,load, nodes,n1,n2
calliope_multi_node_supply	one PV tech with an availability series at two nodes → source_use_max table with a column per node (#7, supply side); same-objective check on all three targets (TestE2EMultiNodeAvailabilityConsistency)	techs,pv,pv, source_unit: per_cap
calliope_operate_limits	operate mode with a limited import and a capacity-bound line (#8): both installed as fixed flow_cap; same-objective check on PyPSA and both Calliope releases (TestE2EOperateLimitsConsistency)	flow_cap: 50, flow_cap: 40
calliope_extensions	annualized costs under a model-wide discount_rate, a system-wide capacity cap, a battery with energy_capacity, depth of discharge and charge rate, a curtailable demand (demand_curtailable), reporting.save_logs	flow_cap_max_systemwide, storage_discharge_depth, flow_cap_per_storage_cap_max, sink_use_max, save_logs
calliope_hub_node	a hub node without technologies joined by two arcs (0.7 and 0.6 dialects)	link_to: hub, link_from: hub, hub: with techs: {}

Calliope 0.6 (calliope-0.6)

The 0.6 target runs every calliope_* scenario its gates accept (all of the above except calliope_piecewise, whose piecewise performance has no 0.6 representation) through its own dialect — TestScenarios pins the 0.6 markers, the goldens under testdata/golden/calliope-0.6/ freeze the emitted documents, and TestE2ECalliope06Corpus solves them on the real calliope-0.6 CLI — plus four 0.6-specific scenarios:

Scenario	Exercises	Key emitted markers
calliope06_typical_days	time_aggregation: typical_days → k-means representative days (0.7 dropped built-in clustering)	apply_clustering, clustering_func: kmeans, k: 2, random_seed
calliope06_spores	first-class experiment.alternatives → run.spores_options + systemwide_cost_max group + spores_score costs	mode: spores, spores_number: 2, slack: 0.1
calliope06_group_constraints	constraint IR + emission limit → group_constraints (capacity cap, minimum production, CO₂ cap)	energy_cap_max: 800, carrier_prod_min, cost_max: {emissions: …}
calliope06_min_emissions	objective: min_emissions → objective_options.cost_class weights	monetary: 0, emissions: 1

AdOpT-NET0

Scenario	Exercises	Key emitted markers
adopt_demand_trade	demand + import/export carrier CSVs	;Demand;, glpk
adopt_storage	Storage_Battery database tech + overrides	Storage_Battery, OPEX_fixed
adopt_pv_climate	physics pv → Photovoltaic + ClimateData.csv	Photovoltaic, ;ghi;
adopt_emission	emission-limit objective	costs_emissionlimit
adopt_wind	physics wind → WindTurbine_Onshore_1500 + wind climate columns	WindTurbine_Onshore_1500, ;ws10;
adopt_pareto	mode: pareto → objective: pareto (+ points via the deprecated solver.options.pareto, warned)	pareto
adopt_monte_carlo	mode: monte_carlo → monte_carlo.N > 0 (via the deprecated solver.options.monte_carlo.N, warned)	monte_carlo (N asserted in TestAdoptRunModeConfig)
adopt_typicaldays	time_aggregation: typical_days → typicaldays.N = periods	typicaldays (N asserted in TestAdoptRunModeConfig)
adopt_node_override	per-node storage overrides in the node-keyed _meme_overrides.json	Storage_Battery, per-node size_max
adopt_extensions	a battery with independent charge/discharge rates and no_simultaneous_charge_discharge, retirement (capacity.decommission), import/export with emission factors, discount_rate, reporting.case_name	charge_rate, discharge_rate, allow_only_one_direction, decommission, case_name
adopt_min_emissions	objective: min_emissions on a PV system	emissions_net, Photovoltaic

The three AdOpT run-mode scenarios are backed by TestAdoptRunModeConfig, which parses the emitted ConfigModel.json and checks the mode actually reached AdOpT's optimization block (objective / monte_carlo.N / typicaldays.N) — the translation described on the targets page that makes those modes genuinely runnable rather than silently downgraded to a cost-optimal plan.