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Feeder definitions (`ieee13`, `ieee34`, `ieee123`), the `SYSTEMS` lookup
dict, the regulator-tap helper (`tap`, `TAP_STEP`), shared simulation
constants (`DT_*`, `V_MIN`/`V_MAX`, `TOTAL_DURATION_S`, `POWER_AUG`),
hardcoded `InferenceModelSpec` presets (`LLAMA_*`, `QWEN_*`,
`MODEL_SPECS`), and the `deploy` / `with_ramp` deployment shortcuts. The
RL-specific scenario randomization, experiment factories, and
`ScenarioOpenDSSGrid` live in `scenarios.py`.
"""
from __future__ import annotations
import math
from fractions import Fraction
from pathlib import Path
from openg2g.datacenter.config import (
InferenceModelSpec,
ModelDeployment,
PowerAugmentationConfig,
ReplicaSchedule,
)
from openg2g.grid.config import TapPosition
PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent
GRID_DATA_DIR = PROJECT_ROOT / "examples"
TAP_STEP = 0.00625 # Standard 32-step regulator: ±10% in 32 steps
def tap(steps: int) -> float:
"""Convert integer tap step to per-unit ratio. E.g., ``tap(14)`` → 1.0875."""
return 1.0 + steps * TAP_STEP
# ── Shared simulation constants ──────────────────────────────────────────────
DT_DC = Fraction(1)
DT_GRID = Fraction(1)
DT_CTRL = Fraction(1)
V_MIN, V_MAX = 0.95, 1.05
TOTAL_DURATION_S = 3600
POWER_AUG = PowerAugmentationConfig(amplitude_scale_range=(0.98, 1.02), noise_fraction=0.005)
# ── Model specs (hardcoded; replaces config.json which master removed) ──────
LLAMA_8B = InferenceModelSpec(
model_label="Llama-3.1-8B",
model_id="meta-llama/Llama-3.1-8B-Instruct",
gpu_model="H100",
task="lm-arena-chat",
precision="bfloat16",
gpus_per_replica=1,
tensor_parallel=1,
itl_deadline_s=0.08,
batch_sizes=(8, 16, 32, 64, 96, 128, 192, 256, 384, 512, 768, 1024),
feasible_batch_sizes=(8, 16, 32, 64, 128, 256, 512),
)
LLAMA_70B = InferenceModelSpec(
model_label="Llama-3.1-70B",
model_id="meta-llama/Llama-3.1-70B-Instruct",
gpu_model="H100",
task="lm-arena-chat",
precision="bfloat16",
gpus_per_replica=4,
tensor_parallel=4,
itl_deadline_s=0.10,
batch_sizes=(8, 16, 32, 64, 96, 128, 192, 256, 384, 512, 768, 1024, 1536, 2048),
feasible_batch_sizes=(8, 16, 32, 64, 128, 256, 512),
)
LLAMA_405B = InferenceModelSpec(
model_label="Llama-3.1-405B",
model_id="meta-llama/Llama-3.1-405B-Instruct-FP8",
gpu_model="H100",
task="lm-arena-chat",
precision="fp8",
gpus_per_replica=8,
tensor_parallel=8,
itl_deadline_s=0.12,
batch_sizes=(8, 16, 32, 64, 96, 128, 192, 256, 384, 512),
feasible_batch_sizes=(8, 16, 32, 64, 128, 256, 512),
)
QWEN_30B = InferenceModelSpec(
model_label="Qwen3-30B-A3B",
model_id="Qwen/Qwen3-30B-A3B-Thinking-2507",
gpu_model="H100",
task="gpqa",
precision="bfloat16",
gpus_per_replica=2,
tensor_parallel=2,
itl_deadline_s=0.06,
batch_sizes=(8, 16, 32, 64, 96, 128, 192, 256, 384, 512),
feasible_batch_sizes=(8, 16, 32, 64, 128, 256, 512),
)
QWEN_235B = InferenceModelSpec(
model_label="Qwen3-235B-A22B",
model_id="Qwen/Qwen3-235B-A22B-Thinking-2507",
gpu_model="H100",
task="gpqa",
precision="bfloat16",
gpus_per_replica=8,
tensor_parallel=8,
itl_deadline_s=0.14,
batch_sizes=(8, 16, 32, 64, 96, 128, 192, 256, 384, 512),
feasible_batch_sizes=(8, 16, 32, 64, 128, 256, 512),
)
ALL_MODEL_SPECS: tuple[InferenceModelSpec, ...] = (LLAMA_8B, LLAMA_70B, LLAMA_405B, QWEN_30B, QWEN_235B)
MODEL_SPECS: dict[str, InferenceModelSpec] = {s.model_label: s for s in ALL_MODEL_SPECS}
SPECS_CACHE_DIR = PROJECT_ROOT / "data" / "specs"
TRAINING_TRACE_PATH = PROJECT_ROOT / "data" / "training_trace.csv"
def deploy(
label: str,
num_replicas: int,
initial_batch_size: int = 128,
) -> tuple[ModelDeployment, ReplicaSchedule]:
"""Shorthand: ``deploy("Llama-3.1-8B", 720, 128)`` -> (ModelDeployment, ReplicaSchedule)."""
return (
ModelDeployment(spec=MODEL_SPECS[label], initial_batch_size=initial_batch_size),
ReplicaSchedule(initial=num_replicas),
)
def with_ramp(
deployment: tuple[ModelDeployment, ReplicaSchedule],
target: int,
*,
t_start: float,
t_end: float,
) -> tuple[ModelDeployment, ReplicaSchedule]:
"""Inject a single ramp into a deploy() result. Convenience for ieee*_experiment factories."""
md, sched = deployment
return (md, sched.ramp_to(target, t_start=t_start, t_end=t_end))
# ── IEEE test feeder constants ───────────────────────────────────────────────
def ieee13() -> dict:
"""IEEE 13-bus test feeder constants."""
return dict(
dss_case_dir=GRID_DATA_DIR / "ieee13",
dss_master_file="IEEE13Bus.dss",
bus_kv=4.16,
source_pu=1.0,
initial_taps=TapPosition(
regulators={
"creg1a": tap(14),
"creg1b": tap(6),
"creg1c": tap(15),
}
),
exclude_buses=("sourcebus", "650", "rg60"),
)
def ieee34() -> dict:
"""IEEE 34-bus (half-line variant) test feeder constants."""
return dict(
dss_case_dir=GRID_DATA_DIR / "ieee34",
dss_master_file="IEEE34Bus.dss",
bus_kv=24.9,
source_pu=1.09,
initial_taps=TapPosition(
regulators={
"creg1a": tap(11),
"creg1b": tap(6),
"creg1c": tap(8),
"creg2a": tap(8),
"creg2b": tap(8),
"creg2c": tap(8),
}
),
exclude_buses=(
"sourcebus",
"800",
"802",
"806",
"808",
"810",
"812",
"814",
"888",
"890",
),
regulator_zones={
"creg1": ["814r", "850", "816", "824", "828", "830", "854"],
"creg2": [
"852r",
"832",
"858",
"834",
"860",
"836",
"840",
"862",
"842",
"844",
"846",
"848",
],
},
)
def ieee123() -> dict:
"""IEEE 123-bus test feeder constants."""
return dict(
dss_case_dir=GRID_DATA_DIR / "ieee123",
dss_master_file="IEEE123Bus.dss",
bus_kv=4.16,
source_pu=1.0,
initial_taps=TapPosition(
regulators={
"creg1a": tap(9),
"creg2a": tap(5),
"creg3a": tap(5),
"creg3c": tap(5),
"creg4a": tap(14),
"creg4b": tap(1),
"creg4c": tap(4),
}
),
exclude_buses=(
"sourcebus",
"150",
"150r",
"149",
"9r",
"25r",
"160r",
"61s",
"610",
"300_open",
"94_open",
"135",
),
zones={
"z1_sw": [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"34",
],
"z2_nw": [
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"35",
"36",
"37",
"38",
"39",
"40",
"41",
"42",
"43",
"44",
"45",
"46",
"47",
"48",
"49",
"50",
"51",
],
"z3_se": [
"52",
"53",
"54",
"55",
"56",
"57",
"58",
"59",
"60",
"61",
"62",
"63",
"64",
"65",
"66",
"67",
"68",
"69",
"70",
"71",
"72",
"73",
"74",
"75",
"76",
"77",
"78",
"79",
"80",
"81",
"82",
"83",
"84",
"85",
"86",
"87",
"88",
"89",
"90",
"91",
"92",
"93",
"94",
"95",
"96",
],
"z4_ne": [
"97",
"98",
"99",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"108",
"109",
"110",
"111",
"112",
"113",
"114",
"115",
"116",
"117",
"118",
"119",
"120",
"121",
"122",
"123",
],
},
)
SYSTEMS = {"ieee13": ieee13, "ieee34": ieee34, "ieee123": ieee123}
# ── Profile helpers ───────────────────────────────────────────────────────────
def _smooth_bump(t: float, t_center: float, half_width: float) -> float:
dt = abs(t - t_center)
if dt >= half_width:
return 0.0
x = dt / half_width
return (1 - x * x) ** 2
def _smoothstep(t: float, t_start: float, t_end: float) -> float:
"""Cubic Hermite smoothstep: zero-derivative at both endpoints."""
if t_end <= t_start:
return 1.0 if t >= t_start else 0.0
if t <= t_start:
return 0.0
if t >= t_end:
return 1.0
x = (t - t_start) / (t_end - t_start)
return x * x * (3.0 - 2.0 * x)
def _irregular_fluct(t: float, seed: float = 0.0) -> float:
"""Irregular fluctuation via superposition of incommensurate frequencies."""
s = seed
f1 = 0.06 * math.sin(2 * math.pi * t / 173.0 + s)
f2 = 0.05 * math.sin(2 * math.pi * t / 97.3 + s * 2.3)
f3 = 0.04 * math.sin(2 * math.pi * t / 251.7 + s * 0.7)
f4 = 0.03 * math.sin(2 * math.pi * t / 41.9 + s * 4.1)
f5 = 0.02 * math.sin(2 * math.pi * t / 317.3 + s * 1.9)
return 1.0 + f1 + f2 + f3 + f4 + f5
def pv_profile_kw(t: float, peak_kw: float, site_idx: int = 0) -> float:
"""Solar PV output (kW per phase) with per-site cloud patterns."""
T = TOTAL_DURATION_S
if site_idx == 0:
trend = 0.85 - 0.30 * (t / T)
cloud = 1.0
cloud -= 0.55 * _smooth_bump(t, 600, 120)
cloud -= 0.40 * _smooth_bump(t, 2100, 180)
fluct = _irregular_fluct(t, seed=0.3)
return max(0.0, peak_kw * trend * max(cloud, 0.05) * fluct)
elif site_idx == 1:
ramp = 0.55 + 0.40 * _smooth_bump(t, 1200, 900)
cloud = 1.0
cloud -= 0.60 * _smooth_bump(t, 1680, 240)
cloud -= 0.25 * _smooth_bump(t, 2400, 150)
fluct = _irregular_fluct(t, seed=2.1)
return max(0.0, peak_kw * ramp * max(cloud, 0.05) * fluct)
else:
ramp = 0.30 + 0.65 * min(1.0, t / 900.0)
cloud = 1.0
cloud -= 0.70 * _smooth_bump(t, 2700, 300)
cloud -= 0.30 * _smooth_bump(t, 1200, 100)
fluct = _irregular_fluct(t, seed=2.0 + site_idx * 3.7)
return max(0.0, peak_kw * ramp * max(cloud, 0.05) * fluct)
def load_profile_kw(t: float, peak_kw: float, site_idx: int = 0) -> float:
fluct_period = 130.0 + site_idx * 37
fluct = 1.0 + 0.06 * math.sin(2 * math.pi * t / fluct_period + site_idx * 1.4)
if site_idx == 0:
base = 0.15 + 0.85 * _smooth_bump(t, 2280, 1400)
surge = 0.20 * _smooth_bump(t, 2280, 180)
return max(0.0, peak_kw * (base + surge) * fluct)
elif site_idx == 1:
base = 0.10
base += 0.50 * _smooth_bump(t, 1500, 600)
base += 0.80 * _smooth_bump(t, 2880, 500)
return max(0.0, peak_kw * base * fluct)
elif site_idx == 2:
base = 0.80 - 0.55 * _smooth_bump(t, 1800, 1200)
surge = 0.70 * _smooth_bump(t, 2520, 400)
return max(0.0, peak_kw * (base + surge) * fluct)
elif site_idx == 3:
base = 0.10 + 0.90 * _smooth_bump(t, 3120, 800)
return max(0.0, peak_kw * base * fluct)
else:
base = 0.10
base += 0.60 * _smooth_bump(t, 1080, 300)
base += 0.75 * _smooth_bump(t, 2100, 350)
base += 0.90 * _smooth_bump(t, 3300, 300)
return max(0.0, peak_kw * base * fluct)
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