MEME 0.2.0-alpha

Added

• Power flow as a PyPSA run mode (ADR-0008): experiment.mode: power_flow
  solves the AC network for a fixed dispatch with lpf() and pf() instead
  of optimizing. The canonical model describes the network itself —
  node.electrical.v_nom, transmission.electrical (kind line or
  transformer, r, x, s_nom, num_parallel, tap_ratio) and
  technology.dispatch (overridable per node) — and experiment.power_flow
  names the slack, the method (newton_raphson or linear),
  distribute_slack and the tolerance. The run writes output/power_flow/:
  convergence.csv, per-bus voltage magnitude, angle and power, per-line and
  per-transformer flows and loading, generator dispatch and a summary.json;
  a snapshot that does not converge fails the run. The API consumer chains a
  plan and a power flow as two calls; MEME holds no state between them.

• Gates around the new inputs: the mode routes through the power_flow
  capability (PyPSA only); power-flow inputs in an optimization mode (a
  dispatch, the electrical blocks) are kept and named in one warning
  while the arcs stay transport links (see Changed); in mode power_flow
  expandable capacities, trade,
  constraints, emission limits and scenarios are rejected, and
  distribute_slack requires a dispatch on the slack technology (PyPSA
  shares the balance in proportion to each generator's set-point, so a slack
  without one would receive none of it).

• Scenario pypsa_power_flow, the example payloads
  examples/pypsa_power_flow*.json (the reference network, the smallest
  valid power flow, a meshed ring with per-node dispatch, and the options:
  linear method, distributed slack, storage sign, a second carrier) and E2E
  checks for convergence, the linear method and a diverging load. The
  examples walkthrough documents the options, the result files and the
  rejected requests.

• Reactive power, voltage control, shunts and controlled transfers for the
  power flow (ADR-0009): technology.electrical with reactive_dispatch
  or power_factor (+ leading), control: pv and voltage_setpoint;
  node.electrical.shunt; transmission.dispatch as a controlled HVDC
  transfer on an arc without impedance; b, g, phase_shift and
  r_per_length/x_per_length on transmission.electrical. The result
  folder gains the reactive tables, the load and link tables and the shunt
  tables. Gates: one way to give Q, voltage set-points for pv generators
  and the slack only and agreeing per bus, an electricity arc needs an
  impedance or a transfer, every AC island needs a generator (an island
  without the named slack is reported as balanced by PyPSA), and the linear
  method says what it ignores. Scenario and example payload
  pypsa_power_flow_reactive, E2E-pinned on the held voltages.

• The electrical network inside the optimization (ADR-0010):
  experiment.network.model: linear_power_flow makes PyPSA solve under
  Kirchhoff's voltage law with Lines and Transformers (expansion from the
  arc's capacity, electrical.max_loading as s_max_pu), and
  network.security.outages runs the security-constrained solve; other
  targets reject the model like a mode. Transmission gains lifetime,
  interest_rate and cost_basis, and PyPSA prices the arc's investment
  and fixed O&M on Links and Lines. Every optimization run writes
  output/results/ (dispatch, flows, storage states, marginal prices,
  capacities.csv, statistics.csv, summary.json).

• PyPSA optimization completeness (ADR-0011): an energy-sized storage
  (storage.energy_capacity) is a Store with charger and discharger Links,
  which honors independent rates, depth of discharge and the energy-capacity
  cost (_storage.json carries the equal-power and rate constraints);
  storage.soc_profile pins a StorageUnit's state of charge;
  capacity.per_unit builds integer modules; decommission.mode: continuous retires existing capacity at its fixed O&M and builds new
  capacity above it; allow_unmet_demand adds priced slack generators;
  copperplate joins every node through a free hub; operation.ramp_start_up,
  ramp_shut_down, standby_cost, initial_uptime, initial_downtime and
  costs.<class>.variable_om_quadratic map onto PyPSA's unit-commitment and
  cost attributes; model.periods with years are investment periods with
  build_year, lifetime and new builds per period. Scenarios and goldens
  for every claim, E2E on the real framework, and the example
  examples/pypsa_planning.json.

Changed

• PyPSA no longer rejects independent storage rates, depth of discharge,
  continuous retirement, unmet demand or copperplate; the energy-capacity
  cost is priced instead of dropped. Calliope and AdOpT-NET0 keep every
  mapping they had; the new fields they have no slot for (soc_profile,
  the unit-commitment extras, the quadratic cost, investment periods) are
  ignored there with a warning. The validation matrix records every verdict
  that moved.
• time.subset and costs.<class>.purchase are rejected on PyPSA and
  AdOpT-NET0 instead of being dropped without a word: neither target has a
  slot (narrow time.start/end; fold the purchase cost into
  investment_per_capacity). Calliope 0.7 warns that area.per_capacity
  is not emitted (0.6 emits resource_area_per_energy_cap). The validation
  matrix records the moved verdicts.
• Power-flow inputs — dispatch, the electrical blocks on technologies,
  arcs and nodes — are ignored with one warning wherever no power flow runs:
  on Calliope and AdOpT-NET0 and in PyPSA's optimization modes alike. A
  dispatch in an optimization mode was rejected before; now one payload
  serves the planning call and the power-flow call (ADR-0009).
• The PyPSA driver parses its run configuration as JSON (json.loads)
  instead of pasting the JSON in as a Python literal, which broke on the
  first boolean setting.

Fixed

• PyPSA state_of_charge_initial is absolute (MWh), but the canonical
  fraction storage.initial_soc was written into it unchanged, so a
  half-full 24 MWh unit started at 0.5 MWh. The fraction is now applied to
  the installed energy (p_nom × max_hours); an expandable storage is
  told it starts from its existing capacity.