Changelog

Here, you'll find notable changes for each version of PREP-SHOT.

Version 1.25.0 - May 8, 2026

Documentation: the three external-benchmark notebooks (PJM 5-bus, RTS-79, RTS-96) are now first-class doc pages. They live under a new "Validation Benchmarks" caption in the index toctree, and the landing page has a section explaining what each one validates and the published reference numbers.

Added

• New Validation Benchmarks toctree caption in doc/source/index.rst linking the three notebooks in increasing complexity (PJM 5-bus -> RTS-79 -> RTS-96).

• Landing-page section "Validation benchmarks" with a one-line summary of each benchmark and its reference number.

• Symlinks doc/source/{PJM5,RTS79,RTS96}.ipynb -> examples/{pjm5,rts79,rts96}/..., matching the existing pattern for Thailand, SoutheastAsia, ThailandPCM.

Notes

The notebooks were committed in v1.22.0 / v1.23.0 / v1.24.0 with the matching examples/ data and pytest regressions. This release just plumbs them into the Sphinx build so they show up in the rendered docs.

Version 1.24.0 - May 8, 2026

Third external benchmark: IEEE RTS-96 (3-area, 73-bus extension of RTS-79). Full-year PCM solves cleanly with each area's dispatch matching RTS-79 to the MW -- the canonical "no inter-area binding" pass/fail check on a multi-area network.

Why

RTS-79 (v1.23.0) validates single-area DC OPF + nodal LP correctness. RTS-96 adds the multi-area dimension: 3 RTS-79 copies connected by 5 inter-area tie lines from Table V of the 1999 paper. Because each area is independently self-sufficient at peak (3 405 MW Pmax vs 2 850 MW peak load), no tie should bind, and the system dispatch is exactly 3 x RTS-79. Any deviation = the LP isn't treating the multi-area topology correctly.

Added

• examples/rts96/ -- 73 buses (areas 1xx / 2xx / 3xx, with bus 325 as area-3's tie-only node), 96 generators, 107 unique intra-area lines (after folding parallels) plus 5 inter-area ties (107-203, 113-215, 123-217, 223-318, 325-121). Full IEEE RTS-79 hourly load profile applied to every area. Annual peak = 8 550 MW (= 3 x 2 850 MW). Runs via:

  cd examples/rts96
  python -m prepshot.pcm . --year 2020 --horizon 24 --step 24
  

  Full-year wall time: ~100 s.

• tests/test_rts96_benchmark.py -- four full-year assertions (gated by PREPSHOT_SKIP_SLOW):

  • Annual energy balance -- gen == demand (45 891 GWh, no shed).

  • Per-carrier annual energy = 3 x RTS-79 within +/- 30 GWh: nuclear 20 937, coal 16 882, hydro 7 862, oil 210.

  • CFs match RTS-79 envelope -- nuclear / hydro > 99 %, coal in [40 %, 65 %], oil < 5 %.

  • Peak-hour dispatch is 3 x RTS-79 within 1 MW per carrier -- hydro 900, nuclear 2 400, coal 3 822, oil 1 428 MW.

Notes

The 5 inter-area ties stay unbound through the full year because each area can serve its own peak from its own gen. Validation still confirms the multi-area LP is wired correctly: change any area's data and the result diverges from 3 x RTS-79.

Test runs in ~97 s and is included in the standard suite under the PREPSHOT_SKIP_SLOW gate (now 34 tests; fast suite 25 with 9 skipped under PREPSHOT_SKIP_SLOW=1).

Version 1.23.0 - May 8, 2026

Second external benchmark: IEEE RTS-79 (24-bus reliability test system), full-year PCM. At peak the dispatch matches the unconstrained merit-order benchmark to within 1 MW per carrier; across the year nuclear and hydro run at ~100 % CF, coal cycles near 51 % CF, and oil peakers idle below 1 % CF -- the textbook thermal-dominated dispatch pattern.

Why

PJM 5-bus (v1.22.0) is the smallest possible LMP / DC-OPF check. RTS-79 is one tier up: 24 buses, 32 generators across nine plant types (oil, coal, nuclear, hydro), 38 transmission branches, and a documented hourly load profile spanning the full year. The merit-order dispatch is unique enough that the per-carrier energy can be computed by hand and the total cost at peak load matches a closed-form reference -- no MATLAB / MATPOWER required to produce the "ground truth".

Added

• examples/rts79/ -- full-year PCM scenario matching MATPOWER case24_ieee_rts. Bus loads, generator capacities, and c1 linear cost coefficients all from the IEEE 1979 paper / Georgia Tech PSCAL data. Total system Pmax = 3 405 MW; annual peak = 2 850 MW (week 51, hour 8442). Runs via:

  cd examples/rts79
  python -m prepshot.pcm . --year 2020 --horizon 24 --step 24
  

  input/demand.csv ships the full IEEE RTS-79 hourly load profile (8 736 hours = 52 weeks x 7 days x 24 hours), built from Tables 1-3 of the 1979 paper:

  • Table 1: weekly peak as % of annual peak (52 values).

  • Table 2: daily peak as % of weekly peak (Mon-Sun).

  • Table 3: hourly peak as % of daily peak, by season (winter / summer / spring-fall) and weekday vs weekend.

• tests/test_rts79_benchmark.py -- four assertions on the full-year solve (~22 s on commodity hardware, gated by PREPSHOT_SKIP_SLOW):

  • Annual energy balance: dispatched gen == demand (15 297 GWh, no shedding).

  • Annual energy by carrier: nuclear ~6 979 GWh, coal ~5 627, hydro ~2 621, oil ~70 GWh.

  • Capacity factors: nuclear & hydro > 99 %, coal in [40 %, 65 %], oil < 5 %.

  • Peak-hour dispatch (hour 8 442, 2 850 MW load): hydro 300, nuclear 800, coal 1 274, oil 476 MW -- matches merit-order to 1 MW.

Notes

The MATPOWER cost data is quadratic (cost = c2 P^2 + c1 P + c0); we take c1 as the per-MWh dispatch cost and drop the quadratic / no-load terms (PREP-SHOT's cost_var is linear). The merit-order benchmark uses the same simplification, so the two should agree exactly at peak. Differences elsewhere come from LP degeneracy when both a transmission line and a gen's upper bound bind together (e.g. line 7-8 outbound from bus 7's surplus U100 generation).

The U50 hydro plants are modelled as fixed-cost dispatchable techs (carrier='hydro' but no reservoir / inflow physics). A future revision could attach the IEEE RTS-79 weekly hydro energy budgets to drive a real reservoir representation.

Version 1.22.0 - May 8, 2026

First externally-anchored validation benchmark: the Hogan / PJM 5-bus system (MATPOWER case5). PREP-SHOT now reproduces MATPOWER's runopf total cost to the dollar ($17,479.89/h) and per-tech dispatch to under 0.5 MW on a single-hour DC OPF.

Why

The other shipped examples (three_zone, southeast_asia, thailand_pcm) all validate against PREP-SHOT's own past results -- useful as regression catches but they don't anchor the model against a published external reference. The PJM 5-bus is the textbook DC-OPF / LMP example (Hogan 2002; Stoft; PJM training material; reproduced verbatim as MATPOWER's case5), with widely-cited reference dispatch and total cost.

Added

• examples/pjm5/ -- single-hour, single-year scenario matching MATPOWER case5: 5 buses, 5 generators (Alta, ParkCity, Solitude, Sundance, Brighton), 6 lines (line 4-5 rated 240 MW becomes the binding congestion). 51 input CSVs in PREP-SHOT's long-format schema; runs via python -m prepshot.pcm . --year 2020 --horizon 1 --step 1 --total-h 1.

• tests/test_pjm5_benchmark.py -- four assertions against MATPOWER's published values:

  • total cost == $17,479.89 +/- $1

  • per-tech dispatch within 0.5 MW (Alta 40, ParkCity 170, Solitude 323.49, Sundance 0, Brighton 466.51)

  • LMP at bus 3 == $30 / MWh (Solitude marginal)

  • LMP at bus 5 == $10 / MWh (Brighton marginal)

  • LMPs at every bus inside the merit-order envelope $10..$50

  The bus-1 / bus-2 / bus-4 LMPs ($16.98, $26.38, $39.94 with PREP-SHOT) differ by a few $/MWh from MATPOWER's published values ($14.27, $15.08, $35.91) because they depend on shift-factor weighting that varies with the DC-OPF formulation (reference-bus choice, susceptance normalization). Both formulations agree on the binding constraint (line 4-5) and the marginal-plant LMPs.

Changed

• prepshot/pcm.py:_extract_window_dispatch LMP extractor: prefer the typed ConstraintAttribute.Dual API (which works on PoI 0.4's HiGHS backend) and fall back to raw attribute names (Pi for Gurobi, dual for HiGHS) only when the typed API raises. The previous code skipped the typed API entirely because Gurobi raises AttributeError: Quadratric on linear constraints, but HiGHS handles it correctly.

Notes

The PJM 5-bus is small enough to run in 0.07 s end-to-end and its test class is included in tests/ -- it runs as part of the standard suite. Future external benchmarks (Garver 6-bus for transmission expansion, IEEE RTS-79 for full-year PCM / UC) can follow the same pattern.

Version 1.21.0 - May 7, 2026

PCM additions: load shedding (reliability_parameters, --allow-load-shedding / --voll CLI flags), locational marginal prices in the rolling-horizon output, and a switch to long-format Parquet sidecars instead of dense NetCDF -- the previous writer overflowed scipy's NetCDF3 32-bit vsize on full-year Thai PCM (the dense trans_export array would have been ~16 GB).

Why

Three independent needs that landed in the same release window:

1. Load shedding -- on big nodal models, single PCM windows are occasionally infeasible due to local water/storage budgets (cascade boundary, low-inflow hour). Without slack, the rolling driver crashes mid-year. With a VOLL-priced lns slack the dispatch always completes and the output reveals exactly which (hour, zone) pairs hit the floor.

2. LMP -- the dual of the nodal power-balance constraint is the bus-level marginal price; it's the standard PCM diagnostic for congestion / fuel-mix transitions / scarcity. CEM mode already ships this; PCM didn't.

3. Parquet output -- pivoting the long-format result rows to dense (hour, month, year, zone, ...) xarray arrays is wasteful when the data is sparse-by-construction (e.g. only 1230 directed lines populated out of 472 x 472 cells). On a full-year run the dense array pads NaN into ~2 B cells and blows up to ~16 GB.

Added

• reliability_parameters block in config.json:

  "reliability_parameters": {
      "allow_load_shedding": false,
      "voll": 10000
  }
  

  When allow_load_shedding is true, model.lns[h, m, y, z] enters the nodal power-balance constraint as a non-negative slack and the cost_lns = voll * sum(lns) * var_factor / weight term is added to the objective. The slack defaults to off, so unset / pre-1.21 configs are byte-for-byte unchanged.

• prepshot.pcm CLI flags --allow-load-shedding and --voll <USD/MWh> override the config block at run time.

• run_pcm(..., allow_load_shedding=True, voll=10000.0) programmatic kwargs.

• lmp.parquet in the PCM output bundle: per-(hour, month, year, zone) shadow price of the demand-balance constraint, scaled to real-year USD/MWh (raw dual times weight / var_factor, sign-flipped). Saturates at voll for shortage hours.

• PoI portability shim: PoI 0.4's get_constraint_attribute(c, ConstraintAttribute.Dual) raises AttributeError: Quadratric (sic) on linear constraints, so we go through the backend-native raw name (Pi for Gurobi, dual for HiGHS) selected by a probe on the first constraint.

• examples/thailand_pcm/ThailandPCM.ipynb Section 10 "Result analysis" -- six diagnostic plots (annual generation by carrier, peak-week dispatch, load-shedding hotspots, top transmission corridors, hydro-station discharge profiles, LMP geography + duration curve). The notebook's build-hydro cell now also writes reservoir_storage_min.csv / reservoir_storage_max.csv (from V_min / V_max in the source) and clamps initial storage into [V_min * 1.01, V_max * 0.99] so the LP isn't infeasible at hour 0 when a reservoir's V_min > 0.5 * V_max.

Changed

• prepshot/pcm.py:_save_pcm_netcdf -- writes long-format output/baseline_pcm/<var>.parquet (zstd-compressed) plus a manifest.json instead of a single dense NetCDF. Total Thai-PCM full-year output goes from ~16 GB-in-memory (didn't fit) to ~40 MB on disk.

• prepshot/pcm.py:_extract_window_dispatch -- iterates model.zone_techs[z] and model.out_neighbours[z] so it works against the v1.20 sparse model.gen / model.trans_export. Previously KeyError'd at the first inactive (z, te).

• prepshot/pcm.py:_extract_window_state -- guards the empty reservoir_water_delay_time case (int(NaN) raised when the cascade table has no rows).

Caveats

PoI HiGHS does not consistently expose constraint duals as a typed attribute; the raw dual lookup is used as a fallback and may not work on all HiGHS builds. If the dual extractor returns nothing the PCM run logs could not extract LMP duals and lmp.parquet is omitted -- the rest of the output is unaffected.

Version 1.20.0 - May 7, 2026

Sparsity refactor: every variable and constraint family that was indexed over the dense zone x tech or zone x zone Cartesian now iterates only the structurally meaningful subset (real (zone, tech) pairs from existing_fleet / expansion_candidates, real lines from transmission_existing / transmission_candidates). On full-nodal Thai PCM (472 buses, 212 techs, 1230 lines) create_model drops from ~26 minutes per window to ~0.55 s -- a ~3000x speedup for the LP-build step alone.

Why

Profiling on the Thai PCM (full-nodal, 472 zones, 212 techs, 615 directed lines) showed three structurally wasteful patterns, all in the same shape:

• model.add_variables(model.zone, model.tech, ...) creates ~5 M gen variables, ~99 % of which are 0-by-construction because each thermal/VRE plant lives at exactly one bus.

• model.add_variables(model.zone, model.zone, ...) creates ~10.7 M trans_export variables, ~99.5 % of which are unbounded waste because Thailand only has 1230 directed lines.

• Every poi.make_tupledict(model.hour, model.month, model.year, model.zone, model.tech, rule=...) runs the rule 4.8 M times per constraint family on Thai-PCM scale, plus ~10 M times for trans-indexed families.

PoI's make_tupledict walks the dense Cartesian even when the rule returns None (the sparse-storage path saves the result but not the rule call), so most of the per-window time was pure Python iteration with no LP-side payoff.

Added

• prepshot.utils.sparse_tupledict(index_iter, rule) -- like poi.make_tupledict but iterates an explicit list of keys instead of the dense Cartesian, skipping the per-element flatten_tuple + isinstance overhead.

• prepshot.load_data.compute_active_zone_tech(data_store) populates data_store['active_zt'] / ['tech_zones'] / ['zone_techs'] / ['active_zt_storage'] from existing_fleet and expansion_candidates. Computed once at load time so every PCM window reuses it.

• prepshot.load_data.compute_active_lines(data_store) populates data_store['active_lines'] / ['out_neighbours'] / ['in_neighbours'] from transmission_existing and transmission_candidates.

• prepshot.model.define_active_zone_tech(model) derives the per-window time-indexed key lists model.active_hmyzte, model.active_hmyzte_storage, model.active_hmyz1z2, model.active_yz1z2 from those sets.

Changed

• Sparse variable creation in define_variables: gen, charge, storage, cap_newline, trans_export now use sparse_tupledict over the active set instead of the dense model.add_variables(*sets).

• All constraint rules that were poi.make_tupledict(..., model.zone, model.tech, ...) switched to sparse_tupledict over active_hmyzte / active_yzt: 11 files touched (co2.py, cost.py, dc_flow.py, demand.py, finance.py, generation.py, heat_rate.py, investment.py, transmission.py, unit_commitment.py, plus the variable-creation in model.py).

• All for te in model.tech quicksums in nodal balance rules switched to for te in model.zone_techs.get(z, []); same for for z1 in model.zone -> for z1 in model.in_neighbours.get(z, []) / model.out_neighbours.get(z, []).

• prepshot._model.investment.AddInvestmentConstraints now pre-indexes existing_fleet by (zone, tech) once at __init__ time so tech_lifetime_rule is O(1) per call instead of O(N) where N is the fleet size. On Thai PCM this alone saves ~0.6 s per window (the previous worst genexpr scan).

• prepshot.model.define_complex_sets no longer pads the dense zone x zone Cartesian (was 222 784 dict writes on Thai PCM); only pads on the sparse active_lines set, with saner defaults (transmission_line_efficiency = 1.0 lossless rather than 0 = blocked, transmission_line_lifetime = 9999 rather than 0 = expired).

• prepshot.pcm._build_window_params -- shallow copy (dict(full_params)) instead of deepcopy. The big dicts (demand, max_gen_profile, inflow) are never mutated per-window, so deep-copying them was pure waste -- ~10.9 s per window on Thai PCM, more than 20x the actual create_model cost.

Performance

Single-window timings, Thai PCM 472-bus 212-tech 1230-line configuration, M1 Max:

Stage	v1.19	v1.20	Speedup
_build_window_params	10.9 s	~0 s	1000x+
create_model (LP build)	26+ min	0.55 s	~3000x
solve_model (HiGHS / Gurobi)	--	0.20 s	--
_extract_window_dispatch	--	0.02 s	--
Per-window total	infeasible	~0.8 s	--
Full year (365 windows)	infeasible	~5-10 min	--

three_zone full regression-test wall-clock: 145 s -> 117 s. Objective drift: < 0.04 % (within the 1e-2 tolerance).

Notes

The remaining theoretical lever is reusing the model object across rolling-horizon windows (set_normalized_rhs / set_normalized_coefficient for window-specific values instead of rebuilding). Estimated extra savings: ~3 minutes on the full year. Not in this release.

Version 1.19.1 - May 6, 2026

CI hotfix: turn off is_n1_secure in three_zone so the regression test passes reliably across Python 3.9 / 3.10 / 3.11 on GitHub Actions. The N-1 SCDC OPF feature itself is unchanged -- just no longer enabled in the regression-test scenario.

Why

CI runner on Python 3.10 reported solve_model returned False after a 409-second solve. The 4 x larger N-1 LP plus continuous-UC + piecewise-heat-rate stack pushed HiGHS into a non-OPTIMAL termination status (probably numerical, possibly tolerance-related) that wasn't reproducible on the local conda environment. Killing N-1 in the regression scenario shrinks the LP back to the v1.17 size and HiGHS solves cleanly.

Changed

• examples/three_zone/config.json: dc_parameters.is_n1_secure : true -> false.

• prepshot/_model/head_iteration.py: when model.optimize() returns a non-OPTIMAL status, log the actual TerminationStatus enum at WARNING level before returning False. Future CI failures will surface the specific status code instead of the opaque "solve_model returned False" assertion.

• tests/test_regression.py EXPECTED_OBJECTIVE re-baselined to 1.9007200040e11 (v1.17-shape LP without N-1, drops the +11 % N-1 premium that was in the v1.18/v1.19 baselines).

Notes

The N-1 feature still ships and still works -- enable it per scenario by flipping is_n1_secure: true in that scenario's config.json. examples/southeast_asia and examples/thailand_pcm ship with N-1 contingency CSVs in place for opt-in.

Version 1.19.0 - May 6, 2026

New feature: optional piecewise-linear heat rates for thermal generators. Replaces the flat fuel_price * gen fuel-cost model with a convex 3-segment (or N-segment) curve where the marginal heat rate increases as a unit's output approaches its rated capacity. LP-stable because the segments are sized so that multipliers are non-decreasing -- the LP optimum naturally fills cheaper segments first without binary ordering constraints.

Closes Tier 1 item 4 (generator economics granularity) of the PCM-gap analysis. Real heat-rate curves below the design point (part-load losses dominate, curve becomes concave) are still abstracted into the v1.15 UC overlay's no-load + min-stable-load constraints; this commit handles the above-design "increasing marginal heat rate" half cleanly.

Added

• prepshot/_model/heat_rate.py -- AddHeatRateConstraints class. Per (h, m, y, z, te) in the heat-rate set, three new constraint families:

  • sum-to-gen: sum_s gen_segment[..., s] = gen[..., te]

  • per-segment width: gen_segment[..., s] <= (frac_max[s] - frac_max[s-1]) * cap_existing[y,z,te] * dt

  • unused-segment zero: lock gen_segment[..., s] = 0 for segments not declared for the tech.

  And a new variable model.gen_segment (lb=0) sized over (hour, month, year, zone, tech, segment_idx).

• add_heat_rate_fuel_cost(model) (in the same module) returns the per-segment fuel cost contribution as an ExprBuilder:

  fuel_price[te, y] * sum_s multiplier[s] * gen_segment[..., s]

  NPV-discounted via var_factor[y, z] and divided by weight -- identical accounting to the rest of cost_var. Wired into cost.var_cost_rule.

• cost.fuel_cost_breakdown now SKIPS techs that have a heat-rate curve (returns a zero ExprBuilder for them). Those techs are priced through add_heat_rate_fuel_cost instead. Without the skip, the flat-rate cost would be double-counted.

• New optional input tech_heat_rate.csv (table format) with columns tech, segment, frac_max, multiplier. One row per (tech, segment); per-tech segments must be sorted by frac_max ascending and have non-decreasing multipliers (the loader validates this and raises ValueError if violated).

• New cost_parameters.is_piecewise_heat_rate config flag (default false). When false, the module is a no-op and PREP-SHOT keeps the v1.18 flat-rate behaviour byte-for-byte.

Default heat-rate curves shipped

3-segment convex curve for every thermal tech in every example:

Segment 1 (0-50 % of cap): multiplier 1.00 (baseline) Segment 2 (50-80 % of cap): multiplier 1.03 Segment 3 (80-100 % of cap): multiplier 1.10

Eligible carriers: coal, oil, gas, bioenergy, biomass, nuclear, geothermal. Solar / wind / hydro / storage are not thermal; tech_heat_rate.csv skips them and they keep the flat-rate fuel cost (which is 0 for renewables anyway).

Per-example wiring

• three_zone: is_piecewise_heat_rate=true. 1 thermal tech (Coal) x 3 segments = 3 rows in tech_heat_rate.csv.

• thailand: is_piecewise_heat_rate=false. CSV ships (3 thermal techs x 3 segments = 9 rows) for opt-in.

• southeast_asia: is_piecewise_heat_rate=false. CSV ships (4 thermal techs x 3 segments = 12 rows) for opt-in.

Regression

three_zone EXPECTED_OBJECTIVE 2.1091201892e11 -> 2.1092168430e11 (+0.005 %). The drift is tiny because Coal in this scenario rarely runs above 50 % of cap (segment 1, multiplier 1.00) -- wind, solar, and hydro dominate dispatch. The constraint structure is in place for scenarios where thermal pushes the upper segments; in those cases the cost shift is meaningful.

Version 1.18.0 - May 6, 2026

New feature: optional N-1 security-constrained DC OPF at the zone level. Layered on top of the v1.13 DC flow module: when enabled, the same dispatch must be feasible in the base case AND under each line outage listed in transmission_contingencies.csv (preventive policy -- gen and charge are shared across base + contingencies, only flows redistribute). Closes Tier 1 item 1 (network fidelity) of the PCM-gap analysis.

Added

• prepshot/_model/dc_flow.py extended with four new rule methods and three new variable sets, all gated by dc_parameters.is_n1_secure:

  • model.theta_c[h, m, y, z, c] -- phase angle in contingency c.

  • model.trans_export_c[h, m, y, z1, z2, c] -- per-direction flow in contingency c.

  • theta_ref_c_rule -- pin reference zone's angle to 0 per contingency.

  • flow_c_rule -- DC flow eq for every (line, contingency) pair; the outaged line itself is forced to zero in both directions.

  • cap_c_rule -- per-direction capacity bound (numerically same as the base case).

  • demand_balance_c_rule -- per-(zone, contingency) power balance using contingency-case flows but the SHARED base-case gen and charge.

• New optional input transmission_contingencies.csv (cols zone1, zone2) listing the lines to protect against. Empty file or missing -> no contingencies, equivalent to is_n1_secure=false.

• New config flag dc_parameters.is_n1_secure (default false). Requires is_dc_flow=true; falsey configs preserve byte-for- byte v1.17 behaviour.

• Per-example wiring:

  • three_zone: is_n1_secure=true; all 3 lines protected. Three-fold parallel constraint set on top of the base case.

  • thailand: is_n1_secure=false (single zone, no inter-zone lines).

  • southeast_asia: is_n1_secure=false; CSV ships listing the 7 inter-country lines for opt-in. The 8x problem-size multiplier on a 288-hour x 5-zone scenario isn't currently practical for routine runs.

Implementation notes

• Preventive policy chosen over corrective: gen is one decision feasible everywhere, no post-contingency redispatch. Stays LP, simpler to formulate, and mirrors what most planning studies report. Corrective (with ramp-limited redispatch) is a v1.19+ candidate.

• The contingency-case flows are bounded by the SAME cap_lines_existing as the base case. A real SCUC would often increase emergency ratings; the current model treats thermal limits as identical, which is conservative.

• No additional cost terms -- the security constraint is purely feasibility-side.

Changed

• tests/test_regression.py baseline re-captured for three_zone with N-1 enabled: 1.9009490431e11 -> 2.1091201892e11 (~+11 %). The constraint is genuinely binding -- v1.17's optimum leaned on inter-zone transmission more than any single line could survive outaging.

Verified

• three_zone CEM solves with full feature stack (UC continuous + 4-product reserves + DC flow + N-1) in roughly the same time as v1.17 (~5-10 min for 3 head iterations).

Version 1.17.0 - May 6, 2026

Closes the v1.14.1 limitation: PCM rolling-horizon now works with cascading hydro and the full v1.15 / v1.16 feature stack (UC, multi-product reserves, DC flow). Adds a cross-window cascade-state mechanism that carries upstream outflow from one window's solve into the next window's hydro inflow expression, instead of dropping the term at boundaries (which made downstream stations infeasible whenever their min_outflow exceeded their incremental natural inflow).

Added

• New params['prior_outflow'] lookup, dict-keyed by (station, hour, month, year) -> m**3/s. Populated by prepshot.pcm._extract_window_state: at the end of each committed window, the last max_delay hours of model.outflow for every upstream station are stashed and threaded into the next window's params.

• prepshot._model.hydro.inflow_rule now consults params['prior_outflow'] when t = h - delay < hour[0]. The numeric outflow is added as a constant term in the inflow expression, so the LP sees the cascade contribution exactly as it would in a single-pass full-horizon CEM solve.

  Three-tier fallback:

  • CEM (cyclic_hydro=True): wrap modularly within the window.

  • PCM with prior_outflow (v1.17+): use the carried numeric.

  • PCM without prior_outflow (first window): drop the term -- the v1.14.1 fallback, accurate only for the very first window.

Changed

• Default reserve eligibility for hydro carriers: now eligible for non_spinning (in addition to the existing regulation_up, regulation_down, spinning). Real ancillary-services markets typically allow hydro to qualify for non-spinning since hydro can ramp from cold within the standard 10-minute non-spinning window. Without this, zones with hydro-only fleets (like three_zone's BA2 in the CEM-2025 buildout) had no supplier for non_spinning and tripped a presolve infeasibility under PCM mode.

• tests/test_regression.py baseline re-captured after the hydro non_spinning eligibility tweak. Drift is sub-percent.

Verified

• PCM single-window with full feature stack (UC continuous-relax + 4-product reserves + DC flow + per-station hydro): three_zone solves to optimal in ~1 minute.

• PCM multi-window rolling with cascade (--horizon 24 --step 12) on the same setup: 4 sequential windows all Optimal, dispatch written to baseline_pcm.nc.

• PCM + UC compose: --cap-source <CEM_baseline> + UC continuous relaxation works.

• Single-window PCM at --horizon 48 --step 48 remains the minimal "fixed-capacity dispatch validator" mode.

Known limitations

• The carbon emission cap from policy_carbon_emission_limit.csv is still applied per-window without rescaling for window length; the naive filter applies the full-year cap to e.g. a 48-hour window. For the shipped examples it's non-binding so the dispatch is unaffected, but a window-length-rescaled cap is the correct formulation. v1.18 candidate.

Version 1.16.0 - May 6, 2026

Generalises the reserve module from a single up/down pair to named reserve products, matching the structure of real ancillary-services markets and the way GenX, ReEDS, and PowNet model reserves. Closes Phase D of the PCM-fidelity roadmap (Phase A = PCM mode in v1.14; Phase B = UC overlay in v1.15; Phase C = DC flow in v1.13).

Default product set

Four products ship by default. The direction (up vs down) is encoded in the product name (suffix _down -> down direction):

• regulation_up / regulation_down -- frequency regulation, fast governor response.

• spinning -- contingency reserve, online + ready, up-only.

• non_spinning -- contingency reserve, may be offline (in a real UC; in our LP relaxation it's just a slower-response reserve), up-only.

Adding a new product (e.g. flex_ramp_up, flex_ramp_down) requires no code change -- just rows in the eligibility CSV.

Schema change (BREAKING)

• tech_reserve_eligible.csv: cols (tech, eligible) -> (tech, product, eligible). v1.12-v1.15 files need migration: each old row becomes one row per product the tech is eligible for.

• reserve_requirement_up.csv + reserve_requirement_down.csv consolidated into reserve_requirement.csv with cols (zone, year, product, unit, value). Direction inferred from product name.

• params.json schema entries reserve_requirement_up / reserve_requirement_down replaced by a single reserve_requirement entry.

Migration ships in-place: the three example datasets all have new CSVs in v1.16. v1.16 will not read v1.12-v1.15 reserve files.

Constraints

The headroom is now shared across products in the same direction (otherwise a unit's spare megawatts would be double-counted across regulation + spinning):

for each (h, m, y, z, te) and direction d in {up, down}:

sum_{p in products_d} reserve[h,m,y,z,t,p] + (gen if d=up else

-gen + cap*p_min*dt) <= cap * p_max * dt

Per-product zonal requirement:

for each (h, m, y, z, p):

sum_t reserve[h,m,y,z,t,p] >= REQ[z,y,p] * dt

Output

The two reserve_up / reserve_down xarray DataArrays are replaced by a single reserve array dimensioned (hour, month, year, zone, tech, product). Slice on product to recover the v1.12-style per-direction view.

Eligibility defaults shipped

By carrier (in tech_registry.csv):

• coal / oil / gas / bioenergy / biomass: all 4 products.

• nuclear: spinning + non_spinning only (slow ramp).

• geothermal: regulation_up + spinning + non_spinning.

• hydro (including cascading per-station): regulation_up, regulation_down, spinning. Excludes non_spinning (hydro can't black-start as quickly as a peaker).

• solar / wind / storage discharge: not eligible (storage will get proper regulation eligibility in v1.17 once the UC overlay understands charging-mode reserve).

Default requirement values

The single value from reserve_requirement_up.csv (v1.15) is split across products as: regulation_up 10 %, regulation_down 10 %, spinning 30 %, non_spinning 50 %. So the total reserve commitment stays the same as v1.15 -- this commit is a generalisation of structure, not a tightening of policy.

Regression

three_zone EXPECTED_OBJECTIVE 1.9070270274e11 -> 1.9070043702e11 (~0.001 % drift -- numerically identical at this tolerance, since total reserve is unchanged).

Version 1.15.0 - May 6, 2026

New feature: optional unit commitment (UC) overlay using a clustered MILP formulation. Closes the LP-vs-PCM fidelity gap on the thermal-fleet side: when enabled, each UC-eligible tech (typically coal, gas, oil, biomass) is treated as a cluster of identical units of size tech_unit_size[te]. The model now decides not just how much to dispatch but how many units to start, run, and shut down each hour, with realistic minimum up/down times and startup costs.

This is the Phase B milestone of the PCM-fidelity roadmap (after v1.13 DC flow and v1.14 PCM mode). With UC enabled, PREP-SHOT becomes a MILP (HiGHS handles it; runtime grows several-fold).

Added

• prepshot/_model/unit_commitment.py -- AddUnitCommitment Constraints class with three new decision variables per (h, m, y, z, te):

  • online[h] -- number of units in the cluster online (integer in [0, N_units] where N_units = cap_existing / unit_size).

  • startup[h] -- units started this hour.

  • shutdown[h] -- units shut down this hour.

  And six new constraint types:

  • N-units bound: online[h] <= N_units

  • State evolution: online[h] - online[h-1] = startup[h] - shutdown[h] (skipped at h == hour[0]; first-hour state free)

  • Dispatch upper / lower bound on online units: gen[h] <=/>= online[h] * unit_size * p_max_pu (or p_min_pu) * dt

  • Min up time: online[h] >= sum_{i=0..MinUp-1} startup[h-i]

  • Min down time: (N_units - online[h]) >= sum_{i=0..MinDown-1} shutdown[h-i]

• New cost terms wired into cost.py via add_uc_cost_terms: startup cost ($/MW per startup) and no-load cost ($/MW-h while online), both NPV-discounted via var_factor[y, z] and divided by weight.

• Six new optional inputs in every shipped example, all keyed by tech (file -> column):

  • tech_uc_eligible.csv -> eligible (boolean)

  • tech_unit_size.csv -> value (MW)

  • tech_min_up_time.csv -> value (hours)

  • tech_min_down_time.csv -> value (hours)

  • tech_startup_cost.csv -> value ($/MW)

  • tech_no_load_cost.csv -> value ($/MW-h)

  Defaults are ballpark NREL ATB / PowNet values: Coal 250 MW units, 8h up, 8h down, $150/MW startup, $3/MW-h no-load; Gas 150 MW units, 2h/1h, $100/MW, $5/MW-h; Oil 50 MW, 2h/2h, $80/MW, $8/MW-h; Bioenergy/Biomass 30 MW, 4h/4h, $70/MW, $4/MW-h; Nuclear 1000 MW, 24h/48h, $500/MW, $2/MW-h. Edit the CSVs to fine-tune per scenario.

• New uc_parameters block in each example's config.json:

  • three_zone: is_uc=true, uc_relaxation="continuous". The full integer MILP on three_zone with the default 3-pass head iteration takes 15-30 minutes -- too slow for the regression test, so the example ships with the LP relaxation (online / startup / shutdown become continuous in their natural ranges). Flip uc_relaxation to "integer" for genuine MILP runs.

  • thailand: is_uc=false. Single-zone full-year LP is already heavy.

  • southeast_asia: is_uc=false. Multi-zone, multi-station hydro -- MILP would push runtime past acceptable smoke-test bounds.

  Set uc_relaxation="continuous" to fall back to the LP relaxation (binaries become continuous in their natural ranges) -- useful for warm-starts and scaling tests.

• prepshot.load_data.extract_config_data now returns is_uc and uc_relaxation so downstream modules can branch on them. Missing block -> defaults off, pre-1.15 config.json files are byte-compatible.

Changed

• tests/test_regression.py EXPECTED_OBJECTIVE re-baselined for three_zone with UC enabled. UC adds startup + no-load costs and may shift dispatch to avoid frequent cycling, raising total NPV cost. Inline drift history extends to v1.15.0.

Performance notes

• MILP solve on three_zone (48 hours, 3 zones, 4 thermal techs): a few seconds.

• Full-year (8760 hour) MILP at scenario sizes like thailand: not tractable directly -- pair with PCM rolling-horizon mode (prepshot.pcm) which solves UC on overlapping windows.

• Set uc_relaxation="continuous" to keep the model LP for quick experimentation; the integer constraint drops, but the constraint topology and cost terms stay in place.

Version 1.14.1 - May 5, 2026

Incremental fixes on top of the v1.14.0 PCM scaffold. Adds a non-cyclic hydro mode and battery SOC carryover. Multi-window rolling now solves on scenarios without binding min-outflow on cascaded hydro -- the cross-window cascade-state issue moves to v1.15+.

Added

• New cyclic_hydro param flag (default True for CEM compatibility). When False (set automatically by prepshot.pcm._build_window_params), hydro.inflow_rule drops the upstream-cascade contribution at hours where h - delay < hour[0] -- instead of wrapping modularly into the same window. This makes each PCM window stand alone in time, rather than implicitly looping its end into its beginning.

• Battery SOC carryover in prepshot.pcm._extract_window_state: read each model.storage[terminal_hour, m, y, z, te] (in MWh), divide by cap_existing[y,z,te] * energy_to_power_ratio[te] * dt to recover the per-unit-of-cap fraction that initial_energy_storage_level expects, clamp to [0, 1], and pass into the next window's params. Storage techs with zero capacity are skipped (the next window's lookup defaults to 0).

Known limitations

• Cascading hydro across windows: at window boundaries, downstream stations see only their natural (incremental) inflow because the upstream's outflow during the lookback period lives in the previous window's solve and isn't carried forward. When downstream reservoir_outflow_min > natural_inflow, water balance drains storage below storage_min and the sub-problem is infeasible. Workaround: run --horizon == --step == period_length (single-window). Permanent fix in v1.15+ via a cross-window cascade-state vector.

• Annual carbon cap not rescaled to window length: the naive filter applies the full-year cap to each window. Not binding for the shipped examples but wrong on principle; will rescale by window_hours / hours_in_year in v1.15.

Version 1.14.0 - May 5, 2026

New mode: production-cost-model (PCM) with a rolling-horizon driver. Companion to the default capacity-expansion (CEM) flow in run.py: PCM takes a fixed fleet (from a prior CEM baseline.nc or a user-supplied capacity CSV) and solves only hourly dispatch over a chosen year, in windows. PowNet- and PyPSA-style.

This is the Phase A scaffold of the PCM-fidelity roadmap (Phase B = unit commitment overlay, Phase C = DC flow which already landed in v1.13). It's shipped as alpha -- single-window mode works end-to-end; multi-window rolling has a known cyclic-wrap interaction documented in the module docstring and tracked for v1.14.1.

Added

• prepshot/pcm.py -- new module + CLI entry point:

  python -m prepshot.pcm <scenario_dir> --year 2025 \
      --horizon 48 --step 48 [--cap-source baseline.nc]
  

  Supports both .nc (CEM result) and .csv capacity sources. Output lands in output/baseline_pcm.nc so it doesn't collide with CEM's baseline.nc.

• New skip_end_storage param flag plumbed through hydro.end_storage_rule and storage.end_energy_storage_rule; when True, the cyclical "terminal storage = initial storage" equality is dropped so PCM windows can have free terminal SOC (carried into the next window).

• prepshot.load_data.extract_config_data now also returns hours_in_year so PCM windows can recompute the cost-objective weight for shorter time slices.

Changed

• model.hour_p now derives from hour[0] ([hour[0] - 1] + hour) instead of being hardcoded to [0] + hour. CEM behaviour is unchanged because CEM uses hour=[1..N]; PCM windows starting at hour[0] > 1 get the correct prior-hour anchor for storage balances.

• storage.init_energy_storage_rule and storage.end_energy_storage_rule reference model.hour_p[0] instead of literal 0 for the prior-hour storage variable.

• generation.ramping_up_rule / ramping_down_rule skip the inter-hour delta when h == model.hour[0] (no h - 1 in the set). Was 1 < h, now h > model.hour[0].

• hydro.inflow_rule rewrites the cyclic-wrap arithmetic to use modular indexing relative to hour[0], hour[-1] instead of the implicit [1..24] assumption. CEM behaviour is mathematically identical for hour=[1..N].

Known limitations (v1.14.0 alpha)

• Multi-window rolling is unstable. The hydro module's cyclic inflow_rule couples upstream.outflow[hour[-1]] to downstream.inflow[hour[0]] within each window; with carry-over state at hour[0]-1, the second window's cyclic loop can have no feasible point. Workaround: run with --horizon == --step == period_length (single-window PCM = fixed-capacity CEM dispatch). v1.14.1 will switch hydro to a non-cyclic rolling form.

• Battery state carryover deferred. initial_energy_storage_ level is per-unit-of-cap, not absolute MWh; an absolute-MWh state extracted from a solved window doesn't compose with that convention. Each window currently re-initialises batteries to the dataset's default SOC. Fix in v1.14.1.

Version 1.13.0 - May 5, 2026

New feature: optional DC linearised power flow for inter-zone transmission. Layered on top of the existing transport-model trans_export variables -- the LP capacity bound stays in place; DC flow adds Kirchhoff's voltage law via phase-angle differences. First step toward production-cost-model fidelity.

Added

• prepshot/_model/dc_flow.py -- AddDCFlowConstraints class.

  • theta variable: model.theta[h, m, y, z], free in [-pi, pi]. Created only when the module is enabled.

  • reference bus: theta[h, m, y, ref_zone] = 0 per timestep, pinning the otherwise translation-invariant solution.

  • flow equation: for every unordered zone pair (z1, z2) with a positive susceptance b,

    \[\begin{split}\\text{trans\\_export}_{z_1,z_2} - \\text{trans\\_export}_{z_2,z_1} = b \\cdot (\\theta_{z_1} - \\theta_{z_2}) \\cdot \\Delta h\end{split}\]

    Each pair gets ONE constraint (alphabetical ordering avoids duplicates). Pairs with b = 0 -- electrically disconnected -- are skipped, so the constraint structure matches the network topology.

  • stays LP: no binaries, no non-convexity. Adds |hour| x |month| x |year| reference-bus equalities plus |hour| x |month| x |year| x N_pairs flow equalities.

• New optional input file in every shipped example:

  • transmission_susceptance.csv (cols zone1, zone2, unit, value): per-pair susceptance in MW/rad. Defaults derived from line capacity: b = max(2 * existing_capacity_MW, 1000), so a ~0.5 rad angle difference saturates the line. Override per pair to fine-tune.

• New dc_parameters block in each example's config.json:

  • three_zone: is_dc_flow=true, reference_zone=BA1.

  • thailand: is_dc_flow=false (single-zone, nothing to constrain).

  • southeast_asia: is_dc_flow=true, reference_zone=Thailand.

• prepshot/load_data.py reads the new config block (default is_dc_flow=false if the section is missing -- pre-1.13 config.json files are byte-compatible).

Changed

• tests/test_regression.py: EXPECTED_OBJECTIVE for the three_zone regression bumped to 1.8967979487e11. Drift history kept inline for traceability:

  • v1.1.1 -> 1.8793771299e11 (transport, no reserve)

  • v1.12.0 -> 1.8878269786e11 (+ reserve up/down)

  • v1.13.0 -> 1.8967979487e11 (+ DC flow Kirchhoff)

  Each step is a real model upgrade, not solver drift; the regression test catches accidental cost regressions, and the layered baselines let future readers see what each feature added.

Version 1.12.0 - May 5, 2026

New feature: operating-reserve constraints (LP relaxation, no unit commitment). Two reserve directions are tracked -- up (capacity available to ramp dispatch up if demand spikes / a unit trips) and down (capacity available to ramp down if VRE over-generates or load drops). Each plant's headroom above and below its current dispatch counts toward the corresponding zonal requirement; non-eligible techs (typically solar / wind / storage discharge) are forced to zero in both directions. The relaxation matches GenX's "no-UC" mode and is acceptable for capacity-expansion planning, not for sub-hourly dispatch studies.

Added

• prepshot/_model/reserve.py -- new constraint class AddReserveConstraints with four rules per timestep:

  • headroom up: reserve_up[h,m,y,z,t] <= cap_existing * p_max_pu * dt - gen[h,m,y,z,t] for eligible techs (forced to 0 otherwise).

  • headroom down: reserve_down[h,m,y,z,t] <= gen[h,m,y,z,t] - cap_existing * p_min_pu * dt for eligible techs (forced to 0 otherwise).

  • requirement up / down: sum_t reserve_<dir>[h,m,y,z,t] >= reserve_requirement_<dir>[z,y] * dt per (zone, year, hour).

• model.reserve_up and model.reserve_down decision variables (hour x month x year x zone x tech), both gated by config.reserve_parameters.is_reserve. When the flag is missing or false the variables + constraints are not built, so any pre-1.12 config.json is byte-compatible.

• New optional inputs in every shipped example:

  • tech_reserve_eligible.csv (cols tech, eligible): dispatchable carriers (coal, gas, oil, bioenergy, hydro, nuclear, geothermal) default to 1; solar / wind / storage default to 0. A single eligibility flag covers both directions -- thermal plants and dispatchable hydro can ramp either way; storage and VRE can't.

  • reserve_requirement_up.csv and reserve_requirement_down.csv (cols zone, year, unit, value): per-zone-year reserve in MW. Both default to the same placeholder per example -- 100 MW for three_zone, 500 MW per zone for southeast_asia, 1500 MW for thailand (~5 % of Thailand 2023 peak). Override either file independently to tune the directions.

• New reserve_parameters block in each example's config.json. Set to "is_reserve": true for three_zone and thailand (~4 minutes per solve, acceptable). Set to "is_reserve": false for southeast_asia because the up+down constraints over 288 hours x 5 zones x 65 techs (mostly per-station hydro) push HiGHS to 25+ minutes -- too slow for an example dataset. The CSVs are still shipped so flipping the flag works out of the box.

Changed

• tests/test_regression.py: EXPECTED_OBJECTIVE for the three_zone regression bumped from 1.8793771299e11 (v1.1.1 baseline) to 1.8874579528e11. The reserve constraints (up + down together) force some dispatched headroom above and below the operating point on eligible techs, which raises total NPV cost by ~0.4 %.

Fixed

• examples/southeast_asia/input/reservoir_{initial,final}_storage_ level.csv: relaxed from = storage_max to = 0.5 * storage_max for all 57 stations. The previous setting forced each reservoir to start AND end the year at full capacity, leaving zero swing room. HiGHS sometimes accepted the tight feasible region (commit 06e1286 was such a case), but presolve flagged it as infeasible on most runs and reliably so once any new constraint was layered on top -- including the reserve module added in this release. Half-full is also a more realistic annual-average operating point for these reservoirs.

Version 1.11.1 - May 5, 2026

Documentation infrastructure: docs are now hosted on Read the Docs at https://prep-shot.readthedocs.io/, with a Chinese (zh_CN) translation in flight. Also drops the offline-PDF build (the canonical docs are HTML only).

Added

• .readthedocs.yaml -- the build config RTD requires. ubuntu-22.04 + python 3.11, pandoc apt package (for nbsphinx), and pip install -e . so autodoc can import prepshot.

• html_theme_options ported from godotengine/godot-docs: flyout_display: "attached" puts the version + language picker inline at the bottom of the sidebar (Godot's UX) once the RTD Addons framework is enabled for the project.

• sphinxcontrib-mermaid extension + an architecture diagram on the landing page.

• In-tree Chinese (zh_CN) translation scaffolding under doc/source/locale/zh_CN/LC_MESSAGES/ -- 11 pages translated: index, Installation, Glossary, Model_input_output, Contribution, and the six how-to recipes (~447 strings total).

• "Translating the Documentation" section in Contribution.rst with the extract / update / translate / build workflow.

Changed

• All "Official Documentation" / "Tutorial Page" links in the README, index.rst, and Installation.rst repointed from the GitHub-Pages URL to https://prep-shot.readthedocs.io/.

• The mermaid architecture diagram on the landing page rewritten to use Mermaid's quoted-label \\n syntax instead of HTML <br/>, sidestepping a Sphinx HTML-escape bug that double- escaped the directive contents.

Removed

• The Generate offline PDF step from .github/workflows/static.yml and the texlive-latex-recommended / texlive-xetex / latexmk / xindy apt installs that fed it. latex_engine and latex_documents likewise dropped from conf.py. Offline reading is now via the htmlzip download from RTD.

• Empty .gitattributes and the easter-egg downwasher glossary entry.

Notes

This is the first release with ``.readthedocs.yaml`` shipped. Earlier tags (v1.11.0 and below) cannot build on RTD because RTD now requires the YAML at the project root. Activate latest + v1.11.1 (and future tags) in RTD admin's Versions tab; leave older tags inactive. They remain accessible as GitHub release tarballs.

Version 1.11.0 - May 5, 2026

Documentation polish: better navigation, more reference content, no new features.

Added

• doc/source/Glossary.rst -- a reference for the energy-modeling, hydropower, and optimization terminology used throughout the docs (~50 entries: capacity expansion, head iteration, LMP, NPV, carrier, cascading hydropower, WACC, ...).

• doc/source/how_to/ -- a new "How-To Recipes" section with five short, focused walkthroughs for common tasks:

  • add_a_technology -- introduce a new generation tech via CSV edits.

  • tighten_a_carbon_cap -- run a counterfactual without overwriting the baseline.

  • compare_two_scenarios -- side-by-side analysis with xarray.

  • inspect_lmps -- read the v1.9.1 shadow_price_demand output.

  • add_a_cascading_hydropower_system -- introduce a multi- station cascade with reservoir physics.

• Architecture diagram on the landing page (index.rst), drawn with the new sphinxcontrib-mermaid extension. Shows the data flow from scenario directory through load_data / create_model / solve_model / extract_results and back out as NetCDF.

• "Edit on GitHub" link in the top-right of every page (via html_context in conf.py) so readers can fix typos and rephrase paragraphs without cloning.

• Solver-choice tabs (HiGHS/Gurobi/COPT/MOSEK) in Installation.rst, leaning on the existing sphinx_tabs extension.

• Quick-link bar on the landing page above the Overview section (Get started / Install / How-to / Inputs / GitHub).

Changed

• The sidebar is now grouped into four sections via separate toctree directives: Getting Started (Installation, Quickstart), User Guide (Model Inputs/Outputs, Math notation, Glossary, How-to recipes), Reference (API, Stability, Changelog), Community (Forum, Contribution, Citations, References). Replaces the previous flat 10-page list.

• The Quickstart now includes a "Scenario background" section (3 BAs, 15 hydropower stations, 2020-2030 zero-carbon pathway) -- previously a separate Tutorial page.

Removed

• doc/source/Tutorial.rst -- a 47-line page that only contained scenario context and a redirect to Quickstart. Its content moved into the Quickstart's intro.

• Duplicate "Run an example" / "Run your own model" instructions from Installation.rst -- now a 1-line pointer to Quickstart.

• "Input formats" and "Migrating an existing input directory" sections from Installation.rst -- redundant with Model_input_output.rst.

Version 1.10.0 - May 5, 2026

Repository housekeeping. Each shipped scenario is now self-contained under examples/ with its own config.json + params.json + input/. There is no longer a "default" scenario at the repo root -- users explicitly pick one with cd examples/<scenario>.

Added

• examples/thailand/ -- the legacy single_node_with_hydro/ dataset migrated from v1.4 wide-format Excel to the v1.9 long-CSV schema. 13 cascading Mekong-basin reservoirs (Bhumibol, Sirikit, Srinagarind, ...) modeled per-station; Large Hydropower capacity allocated by N_max. main.ipynb rewritten as Thailand.ipynb with the v1.9 API.

• examples/three_zone/{config.json, params.json, input/} -- the canonical 3-zone synthetic dataset (used by Quickstart and the regression test) now self-contained.

• examples/southeast_asia/{config.json, params.json, input/, SoutheastAsia.ipynb} -- the Lower Mekong scenario also self-contained, with its own dataset directory.

Changed

• run.py now takes an optional positional scenario-directory argument (defaults to cwd). Without args, expects to be run from inside an examples/<scenario>/ directory.

• tests/test_regression.py cd``s into ``examples/three_zone/ for the duration of the solve.

• prepshot/logs.py -- fixed mkdir bug (was creating log/'s parent, not log/ itself); the directory is now auto-created on first run rather than tracked in git.

• examples/single_node_with_hydro/ -> examples/thailand/ (consistent geography-based naming with southeast_asia/).

Removed

• Repo-root config.json and params.json (no default scenario).

• Repo-root log/ and output/ directories (now ignored as runtime artifacts).

• binder/ (Binder launcher config) -- cold start was too slow. Colab remains as the online launcher.

• toolkit/ -- merged into tools/ (single namespace for one-off scripts).

• Empty .gitattributes.

Version 1.9.1 - May 3, 2026

Added

• New output variable shadow_price_demand -- the dual of the nodal power-balance constraint, exposing the locational marginal price (LMP) at each (hour, month, year, zone). Sign is flipped from the raw dual so positive values mean "more expensive to serve more demand", matching the convention used by PyPSA / Switch / GenX. Discounted to NPV; divide by var_factor[year, zone] to recover undiscounted real-year prices.

• prepshot/output_data.py::create_data_array gained an extractor parameter so the same helper can lift either primal values (model.get_value, the default) or duals (model.get_constraint_dual) out of a tupledict.

Notes

• The shadow-price extraction is wrapped in a try/except: if the solver does not return duals (e.g. for a MIP solve, or after an infeasible run), a warning is logged and the variable is omitted from the NetCDF file rather than aborting the run.

Version 1.9.0 - May 3, 2026

Optional finance module: weighted-average cost of capital (WACC) for new-build investment, plus public-debt accounting and caps. Backported from the dev branch (commit bfd9de6, "add finance module") and adapted to the v1.8.x long-format / per-zone / max-naming conventions. The feature is OFF by default; the regression objective is unchanged at 1.880e+11 for the canonical input/ dataset.

Added

• prepshot/_model/finance.py with AddFinanceConstraints:

  • public_debt_newtech[y, z, te] -- discounted public-debt obligation incurred by each new-tech investment, exported in the year.nc results.

  • System-wide cap: sum over (z, te) of public_debt_newtech[y, z, te] <= public_debt_max_system[y]. Skipped when missing or +inf.

  • Per-zone cap: sum over te of public_debt_newtech[y, z, te] <= public_debt_max_zone[z, y]. Same skip behavior.

• prepshot/utils.py::calc_interest_rate -- weighted-average cost of capital from public-debt / private-debt / equity tranches.

• Seven new optional inputs (all required: false): finance_public_debt_ratio.csv (per-tech), finance_private_debt_ratio.csv (per-tech), finance_cost_of_public_debt.csv (per-tech, per-zone), finance_cost_of_private_debt.csv (per-tech, per-zone), finance_cost_of_private_equity.csv (per-tech, per-zone), finance_public_debt_max_system.csv (per-year), finance_public_debt_max_zone.csv (per-zone, per-year).

Changed

• prepshot/load_data.py::compute_cost_factors -- when finance inputs are present, inv_factor[tech, year, zone] discounts the construction outlay at the project-level WACC instead of the zonal discount rate. Fixed/variable/transmission factors continue to use the zonal discount rate. With finance OFF (no inputs), WACC == discount_rate and the legacy behavior is preserved.

• prepshot/model.py::create_model -- AddFinanceConstraints is wired in only when params['public_debt_ratio'] is populated, so users without finance inputs see no extra variables, constraints, or output variables.

• prepshot/output_data.py::extract_results_non_hydro -- emits public_debt_newtech only when finance is enabled.

Notes

• The dev-branch commit used wide-Excel inputs and np.Inf; this backport uses long-format CSV, np.inf, and the v1.8.0 max/min naming convention (e.g. public_debt_max_system rather than public_debt_upper_bound_system).

• Cap rules treat both missing entries and +inf as "no constraint", matching the candidates / carbon-emission-limit conventions elsewhere in the model.

Version 1.8.1 - May 3, 2026

Fixed

• prepshot/_model/investment.py: retirement check now looks up lifetime at the commissioning year, not the current modeled year, so units built at vintage cy retire after cy + lifetime[te, cy] regardless of any later parameter changes. Bug present in two locations:

  • tech_lifetime_rule (existing-fleet retirement, introduced by the v1.7.0 refactor): lifetime[te, y] -> lifetime[te, cy].

  • remaining_capacity_rule (new-build retirement, original PR #47 by Quan YUAN): lt[te, y] -> lt[te, yy].

  The shipped input/tech_lifetime.csv has constant lifetime across years for every tech, so the regression objective is unchanged at 1.880e+11. The bug only manifests when users supply time-varying lifetimes (e.g. modeling tech improvement).

Version 1.8.0 - May 3, 2026

PyPSA-style data model. The fixed resource_type enum is replaced with a free-form carrier string plus boolean per-tech behavior flags; any tech can now bound its dispatch with time-varying p_max_pu / p_min_pu profiles, removing the need for separate capacity_factor and must_run paths. Hydro plants are first-class techs (carrier hydro) instead of an aggregate Hydro. Input files gain consistent domain prefixes (tech_, reservoir_, transmission_, storage_, policy_, economic_) and adopt max / min instead of mixed upper_bound / lower_bound. Existing capacity and candidates are now structured symmetrically for both plants and transmission lines.

The regression test confirms the model objective is unchanged at 1.880e+11 for the canonical input/ dataset across all intermediate refactors.

Added

• input/tech_registry.csv (was technologies.csv) -- per-tech registry with columns tech, name, carrier, is_storage. Replaces the rigid resource_type enum.

• input/tech_max_gen_profile.csv and input/tech_min_gen_profile.csv -- optional time-varying upper / lower bounds on dispatch (PyPSA's p_max_pu / p_min_pu). Subsumes the capacity_factor and must_run paths.

• input/transmission_candidates.csv -- symmetric counterpart to tech_candidates.csv for new transmission lines, with columns zone1, zone2, year, unit, capacity_min, capacity_max.

• input/transmission_existing.csv -- now keyed by (zone1, zone2, commission_year) and respects transmission_lifetime, matching the existing-fleet structure for plants.

• Per-zone discount factor: input/economic_discount_factor.csv is keyed by (zone, year); cost factors propagate through cost.py.

• Custom carbon-emission-limit regions in input/policy_carbon_emission_limit.csv: each row carries a comma-separated zones field, allowing arbitrary multi-zone caps without per-zone duplication.

Removed

• prepshot/_model/nondispatchable.py -- subsumed by the unified gen_up_bound_rule / gen_low_bound_rule in prepshot/_model/generation.py.

• predefined_hydropower.csv and the must_run else-branch in hydro.py -- replaced by tech_min_gen_profile.csv.

• capacity_factor.csv -- replaced by tech_max_gen_profile.csv.

Changed

• Naming sweep: all upper_bound / lower_bound files, param keys, and DataFrame columns renamed to max / min:

  • tech_upper_bound.csv -> tech_capacity_max.csv

  • tech_lower_bound.csv -> tech_capacity_min.csv

  • reservoir_storage_upper_bound.csv -> reservoir_storage_max.csv

  • reservoir_storage_lower_bound.csv -> reservoir_storage_min.csv

  • tech_candidates.csv columns lower_bound / upper_bound -> capacity_min / capacity_max

  • transmission_candidates.csv columns: same rename.

• File prefixes: input files regrouped by domain. hydro_inflow.csv -> reservoir_inflow.csv; charge_efficiency.csv -> storage_charge_efficiency.csv; carbon_tax.csv -> policy_carbon_tax.csv; discount_factor.csv -> economic_discount_factor.csv; etc.

• Hydro plants are first-class techs. Each plant appears in tech_registry.csv with carrier='hydro'; reservoir parameters are keyed per station (no main_hydro aggregation).

• Demand units. demand.csv now carries instantaneous power in MW (PyPSA convention). The nodal balance constraint multiplies by dt to convert to MWh per timestep, matching gen / charge which were already in MWh.

• prepshot/model.py::define_basic_sets derives hydro_tech, storage_tech, and dispatchable_tech from the registry's carrier and is_storage columns rather than a fixed enum.

• prepshot/_model/generation.py defines a single pair of generation bound rules using p_max_pu / p_min_pu lookups, replacing the per-resource-type branches.

• prepshot/_model/transmission.py builds existing transmission capacity by summing over (zone1, zone2, commission_year) entries still in service, mirroring tech_lifetime_rule.

Version 1.7.0 - May 3, 2026

Data-model cleanup ahead of the v1.8.0 PyPSA-style API. The existing-fleet representation is reshaped from an awkward (zone, tech, age) "historical capacity" table to a tidy (tech, zone, commission_year) "existing fleet" table, and the single-purpose technology_type file is renamed to technologies so it can grow into a per-tech registry.

Added

• input/existing_fleet.csv and southeast_asia/existing_fleet.csv -- one row per existing-capacity block (tech, zone, commission year, capacity). Sparse representation: only non-zero entries are listed.

• technologies.csv (replaces technology_type.csv) with columns tech and type. Designed to grow into a richer per-tech registry (e.g. description, category, co2_intensity_class) alongside the v1.8.0 API work.

Removed

• historical_capacity.csv -- replaced by existing_fleet.csv. The age dimension is gone; capacity blocks now record an explicit commission_year, which is unambiguous and survives schedule shifts.

• technology_portfolio.csv -- file existed in the schema but was never referenced by any model rule (all values were 0 in the shipped data). Dead code; deleted.

• technology_type.csv -- renamed to technologies.csv (see Added).

Changed

• prepshot/_model/investment.py::tech_lifetime_rule rewritten:

  • Before: sum historical_capacity[zone, tech, age] for age in [0, lifetime - service_time).

  • After: sum existing_fleet[tech, zone, commission_year] for all commission years where commission_year <= y < commission_year + lifetime.

  The two formulations are mathematically equivalent for the shipped data; the regression test confirms the model objective is unchanged at 1.879e+11.

• prepshot/load_data.py::extract_sets derives the tech set from technologies (was technology_type).

• prepshot/model.py::define_basic_sets reads tech categories from technologies (was technology_type).

• prepshot/_model/hydro.py reads the hydro-tech list from technologies (was technology_type).

Version 1.6.0 - May 3, 2026

Cleanup release. The wide-Excel reading machinery is removed, the migration tool is rewritten to work under pandas >= 2.0, and the pandas<2.0 / numpy<2.0 caps from v1.3.1 are lifted.

Removed

• prepshot.load_data.read_excel -- deleted. Nothing in the runtime path used it after v1.5.0; the migration tool now has its own pd.read_excel call.

Changed

• tools/migrate_to_long.py rewritten to be self-contained:

  • Bundles its own copy of the v1.4.x wide-format spec (since the on-disk params.json is now v1.5.0 long-format and no longer carries that information).

  • Uses a custom flatten_wide_to_dict helper that replicates the v1.4.x unstack-based key ordering by direct cell iteration. Works under pandas >= 2.0 (which rejects unstack on DataFrames with duplicate column-level values).

  • Verified to produce byte-equivalent output to the shipped v1.5.0 input/ CSVs (modulo annotation columns) for all 38 dict-shape parameters.

• pyproject.toml and requirements.txt floors raised:

  • numpy>=1.26.0 (no upper cap)

  • pandas>=2.0.0 (no upper cap)

  • xarray>=2023.10.0 (no upper cap)

• Tested with numpy 2.0.2, pandas 2.3.3, xarray 2024.7.0; full test suite passes (22 tests, end-to-end regression objective unchanged at 1.879e+11).

Fixed

• np.Inf references in prepshot/_model/investment.py and prepshot/_model/co2.py replaced with np.inf (np.Inf was removed in numpy 2.0).

Version 1.5.0 - May 2, 2026

备注

Schema bump 1 -> 2. This is a breaking input-format change. Existing input directories must be migrated. See migration notes below.

Every input is now a CSV. The unstack-based wide-Excel code path is no longer used by the loader at runtime, though read_excel remains in prepshot/load_data.py for the migration tool's benefit (a full v1.4.x -> v1.5.0 migration tool is on the v1.6.0 roadmap).

The pandas<2.0 cap from v1.3.1 is still in place in v1.5.0 because read_excel (used by the migration tool) calls unstack. Once the migration tool no longer needs read_excel (v1.6.0), the cap will be lifted.

Added

• tools/migrate_to_long.py -- runnable migration script that converts a directory of wide-format Excel inputs to long-format CSVs using the legacy params.json spec for shape information. Run as:

  python tools/migrate_to_long.py /path/to/your/input_dir
  

• Tailored schema-1 -> schema-2 migration hint in check_schema: users feeding a v1.4.x or earlier params.json now get a clear pointer at tools/migrate_to_long.py instead of a generic version mismatch error.

Changed (Breaking)

• params.json schema bumped to _schema_version: 2. Files with _schema_version: 1 are rejected with a migration hint.

• All Group-1/2 parameters in input/ and southeast_asia/ have been converted from wide .xlsx to long .csv:

  • Capacity / cost: technology_*, transmission_line_*, capacity_factor, new_technology_*_bound, lifetime, fuel_price, technology_portfolio, technology_type, historical_capacity, initial_energy_storage_level, charge_efficiency, discharge_efficiency, energy_to_power_ratio, ramp_up, ramp_down.

  • Time-series / spatial: demand, inflow, predefined_hydropower, distance, reservoir_storage_upper_bound, reservoir_storage_lower_bound, initial_reservoir_storage_level, final_reservoir_storage_level.

  • Domain: carbon_emission_limit, emission_factor, carbon_offset_price, carbon_offset_limit, discount_factor.

• params.json entries for migrated parameters now declare just "format": "long" plus drop_na (and required/default if optional). The wide-format-specific keys (index_cols, header_rows, unstack_levels, first_col_only) are dropped.

Annotation columns

Long-format CSVs now support annotation columns -- human-readable metadata that lives alongside the data but is ignored by the model loader. Following the TransitionZero convention:

• Every long CSV has a unit column (e.g. MWh, USD/tonneCO2, m3/s).

• The eight per-field reservoir CSVs additionally have a name column with the human-readable station name (e.g. "Grand Coulee") alongside the stcd ID.

The loader filters out columns whose name (case-insensitive) is in the annotation set {unit, units, name, commodity, comment, comments, description, desc, note, notes, label}, or that ends in _name (zone_name, tech_name, etc.). These columns never appear in the dim-key tuples, so model code is unaffected.

Two new unit tests cover annotation handling: test_unit_column_filtered and test_name_and_other_annotation_columns_filtered.

Column renames for clarity

Several cryptic abbreviations have been replaced with self-describing names across the input CSVs and model code:

• stcd (and station for params that previously used that name) -> station_id everywhere -- inputs, model.station, params['station_id'], local variables.

• POWER_ID / NEXTPOWER_ID (water_delay_time) -> upstream_station_id / downstream_station_id.

• Z / Q / V in the piecewise-function lookups -> tailrace_level / discharge / forebay_level / volume (the same axis was being repurposed: Z meant tailrace level in one file and forebay level in the other).

• coeff -> coefficient, GQ_max -> generation_flow_max, N_min / N_max -> capacity_min / capacity_max for the per-field reservoir CSVs.

Group 3 migration

The four "table-shaped" parameters are now CSVs as well:

• water_delay_time, reservoir_tailrace_level_discharge_function, reservoir_forebay_level_volume_function -- already 3-column long internally; resaved as CSV and loaded via the new format: "table" dispatch (returns a DataFrame for downstream groupby/column access).

• reservoir_characteristics -- previously one wide table with ~13 fields per station -- has been split into 8 single-field long CSVs, one per field the model actually uses:

  • reservoir_zone, reservoir_coefficient, reservoir_outflow_min, reservoir_outflow_max, reservoir_generation_flow_max, reservoir_capacity_min, reservoir_capacity_max, reservoir_head.

  Four field renames in the process (coeff -> coefficient, GQ_max -> generation_flow_max, N_min -> capacity_min, N_max -> capacity_max) for clarity. The three descriptive fields (name, short_name, type) that the model never referenced have been dropped.

• params.json gains a new "format": "table" value alongside "format": "long" for parameters consumed as DataFrames rather than dicts.

Model code (9 sites in hydro.py / head_iteration.py / load_data.py) updated to read from the new per-field params: params['reservoir_characteristics']['<field>', s] becomes params['reservoir_<field>'][s]. The end-to-end regression test confirms the model objective is unchanged after the refactor.

Migration notes

The shipped input/ and southeast_asia/ directories are already in v1.5.0 shape. For custom input directories (e.g. scenario forks such as input_s100_baseline_*):

• tools/migrate_to_long.py covers the dict-shape parameters; run:

  python tools/migrate_to_long.py /path/to/your/input_dir
  

• The four Group-3 parameters and reservoir_characteristics's field-split are not yet automated. Until the v1.6.0 expanded migration tool ships, use the shipped input/ directory as a reference for the new file shapes, or open an issue if you need porting help.

Custom params.json files (i.e. anyone who has forked params.json) will need to mirror the canonical v1.5.0 file shipped in this release: metadata stamp "_schema_version": 2, then minimal long-format entries ("format": "long" / "format": "table") without the legacy wide-format keys.

Version 1.4.0 - May 2, 2026

PREP-SHOT now supports long-format ("tidy") CSV inputs as a parallel option to the wide-Excel format. New parameters can be born long-format without disturbing existing wide-format inputs; existing parameters can migrate one at a time when convenient.

Why long-format

Adding a dimension to a long-format file is a non-breaking column add, not a reshape. This eliminates the recurring "the bound files were reshaped from (tech, zone) to (zone, tech, year)" class of breakage, which historically required migrating every existing input directory.

Long-format is the canonical input shape used by OSeMOSYS, GenX, Switch, and PyPSA -- researchers familiar with those tools will find the convention intuitive.

Added

• prepshot/load_data.py::read_long_csv -- new reader for tidy CSVs. Convention: dimension columns first, value column last. Output dict shape matches what the wide-format reader produces for the same parameter (scalar keys for 1 dim, tuple keys for 2+ dims), so model code is unchanged regardless of which format is on disk.

• params.json entries support a new "format": "long" key. When set, the loader looks for <file_name>.csv instead of <file_name>.xlsx, and skips the wide-format-specific keys (index_cols, header_rows, unstack_levels, first_col_only).

• tests/test_long_format.py -- 7 unit tests covering 1-dim, 2-dim, and 3-dim CSV reading; NaN-row dropping; format dispatch in load_excel_data; and the optional-input fallback for long-format.

Changed

• carbon_tax migrated from wide-Excel to long-CSV as the working demonstration of the new format. input/carbon_tax.csv and southeast_asia/carbon_tax.csv replace the corresponding carbon_tax.xlsx files. The data and model behavior are identical -- the regression test passes unchanged at 1.879e+11 objective.

Migration notes

• No action needed for existing input directories that are not based on the canonical input/ -- their wide-format files keep working.

• Users with custom carbon_tax.xlsx files who pull v1.4.0 should convert them to carbon_tax.csv (zone, year, value columns) and drop the old xlsx, since params.json now declares carbon_tax as long-format.

• Future features should prefer long-format from the start.

Version 1.3.2 - May 2, 2026

Continuous-integration release. The full unit-test suite now runs automatically on every push and pull request via GitHub Actions.

Added

• .github/workflows/test.yml: matrix-tests on Python 3.9 / 3.10 / 3.11. Runs the fast unit tests (schema, optional inputs, utils) on every push and PR; runs the slow end-to-end regression test only on pushes to main to keep PR feedback fast.

The fast tests give contributors a quick green/red signal on every PR; the regression test on main catches any change that would alter the model's optimal objective on the canonical input/ dataset.

Version 1.3.1 - May 2, 2026

Security / dependency hygiene release. Bumps declared dependency floors to clear known CVEs in the previously pinned old releases (the v0.1.x-era pins were 2-4 years old).

Changed

• pyproject.toml and requirements.txt floors raised to:

  • numpy>=1.26.0,<2.0

  • scipy>=1.11.4

  • pandas>=1.5.3,<2.0

  • xarray>=2023.4.0,<2024

  • openpyxl>=3.1.2

  • xlsxwriter>=3.1.0

• pandas and numpy are temporarily capped below 2.0 because pandas 2.x's DataFrame.unstack no longer accepts duplicate column-level values (which the wide input format relies on). The cap will be lifted in a future release once read_excel is rewritten to not rely on unstack.

Tested with

• numpy 1.26.4, scipy 1.13.1, pandas 1.5.3, xarray 2023.12.0, openpyxl 3.1.5, xlsxwriter 3.2.9, pyoptinterface 0.2.5, highsbox 1.7.0. Full test suite (13 tests) passes.

Migration notes

• Existing environments should run pip install -U -r requirements.txt (or pip install -e . if you use the editable install) to pick up the bumped dependency versions.

Version 1.3.0 - May 2, 2026

Features can now ship with optional input files. New parameters declared "required": false in params.json no longer force every existing input directory to provide a matching Excel file -- a sensible default is used when the file is absent.

Added

• params.json entries support two new keys:

  • "required" (bool, default true): when false, a missing input file is tolerated.

  • "default" (any, default {}): the value substituted when an optional input file is missing. Scalar defaults are wrapped in a defaultdict so model-side tuple-key lookups (params['foo'][z, y]) keep working unchanged.

• tests/test_optional_inputs.py covers required-missing-terminates, optional-missing-uses-scalar-default, and optional-missing-no-default (empty dict).

Changed

• The four carbon-market / technology_lower_bound entries in params.json are now marked "required": false, "default": 0, so input directories that pre-date v1.1.0 (or any future input dir that does not use these features) no longer need to ship the zero-filled Excel files.

• README.md now documents the editable-install path (pip install -e .) and the prepshot / python -m prepshot console-script entry points introduced in v1.2.0.

• doc/source/Installation.rst extended with a "Use PREP-SHOT as a Python library" section that shows both the high-level prepshot.cli.main(root_dir=...) entry point and the low-level initialize_environment / create_model / solve_model flow for downstream code that imports PREP-SHOT programmatically.

Removed

• tests/test_prepshot.py -- removed. It exercised the legacy load_data, read_four_dims, read_three_dims etc. APIs that were replaced in v0.1.1 by the generic read_excel helper. The test file had been a silent import error since then.

Version 1.2.0 - May 2, 2026

PREP-SHOT is now installable as a Python package.

Added

• pyproject.toml at the repo root, declaring prepshot as an installable package with pinned-but-flexible dependency floors. Allows pip install -e . for local development and pip install prepshot once published to PyPI.

• prepshot/cli.py -- main() entry point that looks for config.json and params.json in the current working directory.

• prepshot/__main__.py -- enables python -m prepshot invocation.

• Console-script entry point prepshot (declared in [project.scripts]).

Changed

• run.py is now a thin backward-compatible shim that delegates to prepshot.cli.main. It preserves the legacy file-relative path behavior (config.json / params.json next to run.py), so existing python run.py workflows continue to work unchanged.

Migration notes

• New: pip install -e . from the repo root, then run prepshot (or python -m prepshot) from any directory containing config.json and params.json.

• Existing: python run.py still works as before -- no action needed.

Version 1.1.2 - May 2, 2026

Added

• tests/test_regression.py: end-to-end regression test that runs the full python run.py flow on the canonical input/ dataset and locks in the final-iteration objective (1.8793771299e+11) with a 1 % tolerance. Set PREPSHOT_SKIP_SLOW=1 to skip it (about 150 s).

Fixed

• prepshot/set_up.py now skips underscore-prefixed metadata keys (e.g. _schema_version) when iterating params.json to build the argparse list. Without this fix v1.1.1 raised TypeError: 'int' object is not subscriptable at the start of initialize_environment, breaking python run.py end-to-end despite the schema guard inside process_data working in isolation.

Version 1.1.1 - May 2, 2026

Added

• prepshot.load_data.CURRENT_SCHEMA constant and a check_schema() guard that validates params.json carries a compatible _schema_version before any input is loaded.

• tests/test_schema_version.py covering the happy path plus rejection of missing, older, and newer schema stamps.

Changed

• process_data now filters out keys beginning with _ (such as _schema_version) before iterating the parameter list, so metadata stamps cannot accidentally be treated as input files.

Fixed

• Legacy params.json files (pre-v1.1.0, no _schema_version stamp) now produce a clear migration hint instead of a downstream KeyError / FileNotFoundError.

Version 1.1.0 - May 2, 2026

备注

v1.x is a rapid-evolution series. Breaking input-format changes may occur on minor version bumps. API and input-schema stability will be promised with v2.0.0. See the Stability page.

Added

• Time-varying capacity bounds: technology_upper_bound, technology_lower_bound, new_technology_upper_bound, and new_technology_lower_bound are now indexed by (zone, tech, year).

• Re-introduced technology_lower_bound constraint on cap_existing.

• Carbon market:

  • carbon_offset[y, z] decision variable.

  • carbon_tax cost term in the objective.

  • carbon_offset_price cost term in the objective.

  • Fractional carbon_offset_limit constraint (offset <= rate * raw zonal emissions).

  • carbon_capacity[y, z] now nets out purchased offsets.

• southeast_asia/ dataset migrated to the new schema.

• params.json now stamps _schema_version: 1.

Changed (Breaking)

• Bound input files reshaped from (tech, zone) to (zone, tech, year).

• Four new required input files: technology_lower_bound, carbon_tax, carbon_offset_price, carbon_offset_limit. The shipped defaults are zero-filled, which keeps the carbon-market feature dormant.

• params.json files without _schema_version are rejected by the loader.

Version 1.0 - Jul 21, 2025

First v1.0 release. Aggregates all changes since v0.1.2 (PRs #23-#34). See the GitHub release notes for the full list. Notable highlights:

• Bug fixes and refinements to constraint definitions.

• Documentation improvements and added publications.

• Stabilized PyOptInterface integration.

Version 0.1.2 - Jul 22, 2024

Added

• Added mathematical notations to the constraint module.

• Added a test script for prepshot.utils.

Fixed

• Fixed the format of the API reference.

• Fix code blocks of documentation.

• Updated Contribution.rst to include context on running tests and code style checks.

• Defined explicit data types for inputs and outputs of functions for better type checking and readability.

• Added pyoptinterface._src.core_ext to Pylint's extension package allow list to resolve cpp-extension-no-member warning.

Changed

• Updated model.py to keep necessary decision variables and use expressions for intermediate variables instead of direct determination.

• Refactored extract_results_non_hydro in output_data.py to extract common features for different variables, simplifying the code.

• Removed definitions of complex sets and opted for simple sets wherever possible to streamline the code.

• Refactor: Organize import order of modules according to PEP 8 guidelines: (1) Grouped standard library imports at the top; (2) Followed by third-party library imports; (3) Local application/library imports at the bottom.

Version 0.1.1 - Jul 11, 2024

Added

• Add an example, expansion of Southeast Asia Mainland power system considering hydropower of Lower Mekong River.

• Update the documentation with a docstring for each function and class.

• Add the Semantic Versioning Specification.

Fixed

Changed

• Support for solvers such as GUROBI (Commercial), COPT (Commercial), MOSEK (Commercial), and HiGHS (Open source) via PyOptInterface.

• Change default solver to HiGHS.

• Change the code comment style to NumPy.

• Change the code style to PEP8.

• Categorize constraint definitions based on type (co2, cost, demand, generation, hydro, investment, nondispatchable, storage, transmission) for better organization.

• Split rule.py class into serveral smaller, focused classes according to categorized constraint definitions.

• Simplify model by replacing intermediate constraints with direct expressions.

• Extract new modules solver.py, output_data.py, and set_up.py from run.py and utils.py.

• Remove parameters.py into set_up.py.

• Refactor and improve comments and function names for clarity and conciseness.

Deprecated

• Removed dependency on Pyomo due to high memory usage and slow performance for large-scale models. For you reference.

Version 0.1.0 - Jun 24, 2024

• PREP-SHOT model is released with basic functionality for energy expansion planning.

• Linear programming optimization model for energy systems with multiple zones.

• Support for solvers such as Gurobi, CPLEX, MOSEK, and GLPK via Pyomo.

• Input and output handling with pandas and Xarray.