-Vessim is a **co-simulation testbed for microgrids**\*
+Vessim is a Python library for simulating datacenter energy systems.
-Vessim lets you model how loads, (renewable) generation, energy storage, and the public grid interact, and test control and dispatch strategies against them, in pure simulation or with **real software and hardware** in the loop.
+Vessim models the interaction between datacenter demand and energy infrastructure, helping you understand how design and control decisions affect cost, carbon emissions, and resilience.
-*\*By "microgrid" we mean any local energy system that combines loads, generation, and storage behind a grid connection. Grids can scale from small battery-constrained systems to a GW-scale datacenter campus.*
+[Documentation](https://vessim.readthedocs.io/en/latest/) · [Getting started](https://vessim.readthedocs.io/en/latest/getting_started/) · [Explore example results](https://vessim.readthedocs.io/en/latest/vessim_viewer/)
-Check out the [website and documentation](https://vessim.readthedocs.io/en/latest/)!
+- **Simulation studies:** Compare infrastructure and control strategies across demand, weather, and operating scenarios.
+ Explore design choices through parameter sweeps, optimization, or probabilistic simulations.
+- **Live modeling:** Connect real applications to a simulated energy system through REST APIs.
+ Test how a scheduler or controller responds to changing power availability.
+- **Digital twins:** Model an energy system alongside the real installation using live measurements.
+ Explore possible futures, such as prolonged low renewable generation or a grid outage.
-## What can I do with Vessim?
+## Getting started
-Vessim helps you understand and optimize how loads, energy sources, and storage interact.
-It was originally designed for energy-aware and carbon-aware data centers, but has also been used for other use cases such as battery price arbitrage and demand response for cooling warehouses.
-
-- **Control and dispatch strategies**: Test battery charging/discharging, load-shifting, and price- or carbon-driven dispatch.
-- **Demand response and power outages**: Simulate demand response signals or power outages to understand your system's flexibility and test mitigation strategies.
-- **Microgrid composition**: Experiment with adding solar panels, wind turbines, or batteries to see how they would affect your energy costs and carbon emissions.
-- **Energy- and carbon-aware computing**: Develop applications that adapt their energy consumption to the carbon intensity and price of electricity.
-
-Vessim can simulate multiple distributed microgrids in parallel and easily integrates historical datasets and new simulators.
-Vessim’s software-in-the-loop capabilities let you run real systems against simulated microgrids. Connect live data sources like Prometheus and interact through REST APIs.
+Install Vessim with Python 3.9 to 3.15:
+```console
+python -m pip install vessim
+```
-## Simple Example
+This example models three days of datacenter demand, solar generation, and battery operation.
+Surplus solar power charges the battery, which supplies demand when solar generation falls.
+The grid supplies any remaining deficit and receives surplus power once the battery is full.
-The scenario below simulates a microgrid with a computing system drawing 700W, a solar panel, and a 1.5 kWh battery.
+Download [solar_example.csv](https://raw.githubusercontent.com/dos-group/vessim/main/datasets/solar_example.csv) into your working directory, then run:
```python
import vessim as vs
-environment = vs.Environment(sim_start="2022-06-09", step_size=300)
+# Scale the solar profile to a 2.4 kW installation.
+solar = vs.Trace.from_csv("solar_example.csv", column="Berlin", scale=2400)
-environment.add_microgrid(
- name="datacenter",
+env = vs.Environment(sim_start="2026-01-01", step_size=5 * 60)
+env.add_microgrid(
+ name="site",
actors=[
- vs.Actor(name="server", signal=vs.StaticSignal(value=700), consumer=True),
- vs.Actor(name="solar_panel", signal=vs.Trace.from_csv(
- "datasets/solar_example.csv", column="Berlin", scale=5000
- )),
- ],
- dispatchables=[
- vs.SimpleBattery(name="battery", capacity=1500, initial_soc=0.8, min_soc=0.3)
+ vs.Consumer("load", signal=700),
+ vs.Producer("solar", signal=solar),
],
+ dispatchables=[vs.SimpleBattery("battery", capacity=1000), vs.GridConnection()],
)
+recorder = env.add_controller(vs.Recorder("results/basic_example"))
+env.run(until=3 * 24 * 3600)
+
+metrics = recorder.metrics["microgrids"]["site"]
+print(f"Grid import: {metrics['grid_import'] / 1000:.2f} kWh")
+print(f"Grid export: {metrics['grid_export'] / 1000:.2f} kWh")
+```
-environment.add_controller(vs.CsvLogger("results/my_experiment"))
-environment.run(until=24 * 3600)
+```text
+Grid import: 26.71 kWh
+Grid export: 11.08 kWh
```
-Check out the [Getting Started walkthrough](https://vessim.readthedocs.io/en/latest/getting_started/) and [`examples/`](examples/) for software-in-the-loop simulations.
+[Explore this example in your browser](https://vessim.readthedocs.io/en/latest/viewer/index.html?study=getting-started#results), or open your saved results locally:
+```console
+python -m vessim view results/basic_example
+```
-## Installation
+The [Getting Started walkthrough](https://vessim.readthedocs.io/en/latest/getting_started/) explains how the components work together.
+To use your own data, see [Actors and Signals](https://vessim.readthedocs.io/en/latest/concepts/signals/).
-You can install the [latest release](https://pypi.org/project/vessim/) of Vessim
-via [pip](https://pip.pypa.io/en/stable/quickstart/):
+## Explore further
-```
-pip install vessim
-```
+| Example | What you can explore |
+| --- | --- |
+| [Exploring microgrid configurations](examples/infrastructure_study.py) | Compare wind, solar, and battery configurations over a year. [View results](https://vessim.readthedocs.io/en/latest/viewer/index.html?study=infrastructure-year#results) |
+| [Optimization](https://vessim.readthedocs.io/en/latest/concepts/optimization/) | Search for designs with different cost and emissions trade-offs. [View results](https://vessim.readthedocs.io/en/latest/viewer/index.html?study=optuna-search#results) |
+| [Dispatch strategies](examples/strategy_comparison.py) | Compare control policies across demand scenarios. [View results](https://vessim.readthedocs.io/en/latest/viewer/index.html?study=dispatch-comparison#results) |
+| [Demand response](examples/demand_response.py) | Adjust workload concurrency under a flexible grid connection agreement |
+| [Custom metrics](examples/custom_metrics.py) | Calculate electricity costs, emissions, and your own study objectives |
-If you require software-in-the-loop capabilities, install the `sil` extension:
+[Run the examples locally](examples/README.md) with a shared setup and reproducible commands.
-```
-pip install vessim[sil]
-```
+For [live measurements and REST control](https://vessim.readthedocs.io/en/latest/concepts/live/), install `pip install 'vessim[live]'` and use `vs.Environment.live()`.
+To [connect external simulators](https://vessim.readthedocs.io/en/latest/concepts/coupling/) through Mosaik, install `pip install 'vessim[mosaik]'`.
## Publications
-If you use Vessim in your research, please cite our paper:
+If you use Vessim in your research, please cite:
+
+Philipp Wiesner, Ilja Behnke, Paul Kilian, Marvin Steinke, and Odej Kao.
+[**Vessim: A Testbed for Carbon-Aware Applications and Systems.**](https://dl.acm.org/doi/pdf/10.1145/3727200.3727210)
+*ACM SIGENERGY Energy Informatics Review 4 (5)*, 2024.
-- Philipp Wiesner, Ilja Behnke, Paul Kilian, Marvin Steinke, and Odej Kao. "[Vessim: A Testbed for Carbon-Aware Applications and Systems.](https://dl.acm.org/doi/pdf/10.1145/3727200.3727210)" _ACM SIGENERGY Energy Informatics Review 4 (5)_. 2024.
+More papers are listed in the [documentation](https://vessim.readthedocs.io/en/latest/publications/)!
-For details in Vessim's software-in-the-loop simulation methodology, refer to:
-- Philipp Wiesner, Marvin Steinke, Henrik Nickel, Yazan Kitana, and Odej Kao. "[Software-in-the-Loop Simulation for Developing and Testing Carbon-Aware Applications.](https://doi.org/10.1002/spe.3275)" _Software: Practice and Experience, 53 (12)_. 2023.
+## About
-For more related papers and concrete use cases, please refer to the [documentation](https://vessim.readthedocs.io/en/latest/publications).
+Vessim was originally developed at the research group for [Distributed and Operating Systems](https://distributedsystems.berlin/) at TU Berlin. It is now maintained at the [Machine Learning Systems Lab](https://mlsys.cst.cam.ac.uk/) at the University of Cambridge.
diff --git a/benchmarks/README.md b/benchmarks/README.md
new file mode 100644
index 00000000..8cc7c737
--- /dev/null
+++ b/benchmarks/README.md
@@ -0,0 +1,82 @@
+# Benchmarks
+
+These scripts measure simulation speed, recorder overhead, and viewer indexing.
+Run them from a checkout installed with `pip install -e .`.
+Install `pip install -e '.[mosaik]'` only when comparing backends.
+Write reports under `results/` to keep generated measurements out of the repository.
+
+## Backend workloads
+
+```console
+python benchmarks/run.py --backend native --output results/benchmarks/native.json
+python benchmarks/run.py --backend mosaik --output results/benchmarks/mosaik.json
+```
+
+Six workloads cover static inputs, traces, CSV recording, multiple microgrids, mixed sampling intervals, and controller feedback.
+Reports contain median setup and execution times, individual timing samples, peak traced Python memory, and trajectory checksums.
+Each workload retains its trajectory in memory with metrics disabled, and the CSV case additionally writes it to disk.
+Repeated runs must produce the same checksum.
+Imports and trace loading are outside the timed workloads, except for lazy backend initialization on first use.
+Memory measurements run separately from timing measurements.
+
+Use `--steps` and `--repeats` to change the workload size.
+To profile all six workloads separately:
+
+```console
+python benchmarks/run.py --output results/benchmarks/native.json --profile results/benchmarks/profiles
+python -m pstats results/benchmarks/profiles/traces.prof
+```
+
+The fixed `input.csv` is also used to compare native and Mosaik trajectories in `tests/test_backends.py`.
+The tests compare every timestamp and recorded field, allowing floating-point roundoff in numeric values.
+Separate behavioral tests check power balance, battery state, sampling, and controller ordering against expected results.
+Historical timing reports remain available in Git history.
+
+## Study throughput and memory
+
+```console
+python benchmarks/studies.py --runs 1000 --output results/benchmarks/studies-native.json
+```
+
+This measures sequential scenarios with a shared trace and metrics-only recording, including model construction and cleanup.
+It also compares Python allocation peaks for no recorder, metrics only, and full in-memory trajectories at 1,000 and 10,000 steps.
+Use `--backend mosaik` for a backend comparison, or `--runs`, `--steps`, and `--memory-steps` for shorter checks.
+Traced allocations are not total process memory.
+
+## Annual workloads and parallel execution
+
+```console
+python benchmarks/native_study.py --quiet --output results/benchmarks/annual
+```
+
+This uses the infrastructure example's synthetic inputs with three reference configurations.
+It measures the cost of observations, built-in metrics, custom metrics, and recording to memory or disk over a simulated year.
+It also includes five-minute simulations, profiles, and throughput at different worker counts.
+Reports separate input preparation, model setup, and simulation time.
+Parallel measurements include process startup and writing experiment records, and check that results agree across worker counts.
+
+For a short throughput check:
+
+```console
+python benchmarks/native_study.py --quiet --throughput-only --jobs 6 --workers 1 2 --output results/benchmarks/parallel
+```
+
+## Viewer
+
+```console
+node viewer/benchmark.mjs 10000
+```
+
+This measures indexing synthetic configurations in JavaScript, excluding browser rendering and network requests.
+For browser checks with 10,000 recorded summaries, use the existing viewer test fixtures:
+
+```console
+npm --prefix viewer ci
+npm --prefix viewer run build
+python viewer/tests/prepare.py
+python -m vessim view results/viewer-release/input/large
+```
+
+Both viewer datasets are synthetic fixtures, not simulation results.
+Compare timings on the same machine with the same dependencies and workload settings.
+Profiling adds overhead, so profile timings should not be compared with ordinary execution times.
diff --git a/benchmarks/input.csv b/benchmarks/input.csv
new file mode 100644
index 00000000..d7536f8b
--- /dev/null
+++ b/benchmarks/input.csv
@@ -0,0 +1,1002 @@
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diff --git a/benchmarks/native_study.py b/benchmarks/native_study.py
new file mode 100644
index 00000000..31aba37e
--- /dev/null
+++ b/benchmarks/native_study.py
@@ -0,0 +1,269 @@
+"""Benchmark annual simulation throughput and recorder overhead.
+
+Run with .venv/bin/python benchmarks/native_study.py.
+Writes raw timings and profiles under results/benchmark-native.
+No simulation models or engine methods are replaced.
+"""
+
+from __future__ import annotations
+
+import argparse
+from concurrent.futures import ProcessPoolExecutor
+import cProfile
+import io
+import json
+import multiprocessing
+import os
+from pathlib import Path
+import platform
+import pstats
+import statistics
+import sys
+import tempfile
+import time
+
+import numpy as np
+import pandas as pd
+from loguru import logger
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT / "examples"))
+
+import vessim as vs # noqa: E402
+from infrastructure_study import inputs # noqa: E402
+
+CONFIGS = [
+ dict(wind_kw=0, solar_kw=0, battery_kwh=0, battery_c_rate=0.5),
+ dict(wind_kw=1500, solar_kw=2000, battery_kwh=4000, battery_c_rate=0.25),
+ dict(wind_kw=5000, solar_kw=6000, battery_kwh=12000, battery_c_rate=1.0),
+]
+
+
+class Observer(vs.Controller):
+ """Request normal detached state observations without recording metrics."""
+
+ def step(self, now, microgrid_states):
+ pass
+
+
+def run_one(config, seed, mode="metrics", step_seconds=3600, output=None):
+ started = time.perf_counter()
+ frame = inputs(seed=seed, step_seconds=step_seconds)
+ hours = (frame.index.to_numpy() / 3600) % 24
+ frame["price"] = np.where((hours >= 17) & (hours < 21), 0.30, 0.15)
+ frame["export_price"] = 0.04
+ input_done = time.perf_counter()
+ battery = (
+ vs.SimpleBattery("battery", config["battery_kwh"] * 1000, c_rate=config["battery_c_rate"])
+ if config["battery_kwh"]
+ else None
+ )
+ with vs.Environment("2026-01-01", step_size=step_seconds, backend="native") as env:
+ env.add_microgrid(
+ name="site",
+ actors=[
+ vs.Producer("wind", vs.Trace(frame.wind_cf * config["wind_kw"] * 1000)),
+ vs.Producer("solar", vs.Trace(frame.solar_cf * config["solar_kw"] * 1000)),
+ vs.Consumer("datacenter", vs.Trace(frame.load_kw * 1000)),
+ ],
+ dispatchables=([battery] if battery is not None else []) + [vs.GridConnection()],
+ grid_signals={
+ key: vs.Trace(frame[key]) for key in ("carbon", "price", "export_price")
+ },
+ )
+ recorder = None
+ if mode == "observer":
+ env.add_controller(Observer())
+ elif mode != "none":
+ custom = (
+ {
+ "grid_emissions": vs.GridImportEmissions("site", "carbon"),
+ "electricity_cost": vs.ElectricityImportCost("site", "price"),
+ "export_revenue": vs.ElectricityExportRevenue("site", "export_price"),
+ }
+ if mode != "physical"
+ else {}
+ )
+ recorder = env.add_controller(
+ vs.Recorder(
+ output,
+ timeseries=mode in ("trajectory_memory", "trajectory_csv"),
+ custom_metrics=custom,
+ parameters={**config, "seed": seed},
+ )
+ )
+ setup_done = time.perf_counter()
+ result = env.run(365 * 86400)
+ run_done = time.perf_counter()
+ assert result.steps == 365 * 86400 // step_seconds
+ checksum = recorder.metrics["microgrids"]["site"] if recorder is not None else None
+ return {
+ "config": config,
+ "seed": seed,
+ "mode": mode,
+ "step_seconds": step_seconds,
+ "input_seconds": input_done - started,
+ "setup_seconds": setup_done - input_done,
+ "run_seconds": run_done - setup_done,
+ "total_seconds": time.perf_counter() - started,
+ "steps": result.steps,
+ "metrics": checksum,
+ }
+
+
+def worker(task):
+ config, seed, output = task
+ return run_one(config, seed, output=output)
+
+
+def configure_logging(quiet):
+ if quiet:
+ logger.disable("vessim")
+
+
+def summary(rows):
+ return {
+ name: {
+ "median": statistics.median(row[name] for row in rows),
+ "min": min(row[name] for row in rows),
+ "max": max(row[name] for row in rows),
+ }
+ for name in ("input_seconds", "setup_seconds", "run_seconds", "total_seconds")
+ }
+
+
+def main():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--output", type=Path, default=ROOT / "results/benchmark-native")
+ parser.add_argument("--repeats", type=int, default=6)
+ parser.add_argument("--jobs", type=int, default=100)
+ parser.add_argument("--workers", nargs="+", type=int, default=[1, 2, 4, 8])
+ parser.add_argument("--quiet", action="store_true", help="Disable Vessim log messages")
+ parser.add_argument("--throughput-only", action="store_true")
+ args = parser.parse_args()
+ if args.repeats < 1 or args.jobs < 1 or any(w < 1 for w in args.workers):
+ parser.error("repeats, jobs and workers must be positive")
+ configure_logging(args.quiet)
+ args.output.mkdir(parents=True, exist_ok=True)
+ report = {
+ "system": {
+ "python": sys.version,
+ "platform": platform.platform(),
+ "machine": platform.machine(),
+ "logical_cpus": os.cpu_count(),
+ "numpy": np.__version__,
+ "pandas": pd.__version__,
+ },
+ "scope": "365 days, three actors, battery where configured, grid, six traces",
+ "input_source": "examples/infrastructure_study.py:inputs",
+ "logging": "disabled" if args.quiet else "default",
+ "ablations": {},
+ "parallel": [],
+ }
+
+ def save():
+ (args.output / "timings.json").write_text(json.dumps(report, indent=2) + "\n")
+
+ # Warm imports and allocation paths before collecting unprofiled timings.
+ run_one(CONFIGS[1], 9999)
+ with tempfile.TemporaryDirectory(prefix="native-benchmark-", dir=args.output) as scratch:
+ modes = [
+ "none",
+ "observer",
+ "physical",
+ "metrics",
+ "metrics_disk",
+ "trajectory_memory",
+ "trajectory_csv",
+ ]
+ rows = {mode: [] for mode in modes}
+ # Rotate execution order so later modes do not always get a warmer CPU.
+ for repeat in range(0 if args.throughput_only else args.repeats):
+ order = modes[repeat % len(modes) :] + modes[: repeat % len(modes)]
+ for mode in order:
+ output = (
+ Path(scratch) / f"{mode}-{repeat}"
+ if mode in ("metrics_disk", "trajectory_csv")
+ else None
+ )
+ rows[mode].append(
+ run_one(CONFIGS[repeat % len(CONFIGS)], 10000 + repeat, mode, output=output)
+ )
+ print(f"Ablations {repeat + 1}/{args.repeats}", flush=True)
+ for mode in modes:
+ if not rows[mode]:
+ continue
+ report["ablations"][mode] = {"summary": summary(rows[mode]), "runs": rows[mode]}
+ print(mode, json.dumps(summary(rows[mode])), flush=True)
+ save()
+
+ if not args.throughput_only:
+ fine = [run_one(CONFIGS[1], 10000 + i, step_seconds=300) for i in range(3)]
+ report["five_minute"] = {"summary": summary(fine), "runs": fine}
+ save()
+ print("Five-minute", json.dumps(summary(fine)), flush=True)
+
+ for mode in (() if args.throughput_only else ("none", "metrics", "trajectory_csv")):
+ profiler = cProfile.Profile()
+ for i in range(3):
+ profiler.runcall(
+ run_one,
+ CONFIGS[1],
+ 10000 + i,
+ mode,
+ output=(
+ Path(scratch) / f"profile-{mode}-{i}" if mode == "trajectory_csv" else None
+ ),
+ )
+ profiler.dump_stats(str(args.output / f"{mode}.prof"))
+ stream = io.StringIO()
+ stats = pstats.Stats(profiler, stream=stream)
+ stats.strip_dirs().sort_stats("cumulative").print_stats(50)
+ stats.sort_stats("tottime").print_stats(40)
+ (args.output / f"{mode}-profile.txt").write_text(stream.getvalue())
+ print(f"Profiled {mode}", flush=True)
+
+ configs = CONFIGS
+ report["parallel_configuration_source"] = "three reference configurations"
+ reference = None
+ for workers in args.workers:
+ tasks = [
+ (configs[i % len(configs)], 10000 + i, Path(scratch) / f"parallel-{workers}-{i}")
+ for i in range(args.jobs)
+ ]
+ started = time.perf_counter()
+ if workers == 1:
+ results = list(map(worker, tasks))
+ else:
+ with ProcessPoolExecutor(
+ max_workers=workers,
+ mp_context=multiprocessing.get_context("spawn"),
+ initializer=configure_logging,
+ initargs=(args.quiet,),
+ ) as pool:
+ results = list(pool.map(worker, tasks, chunksize=1))
+ elapsed = time.perf_counter() - started
+ metrics = [r["metrics"] for r in results]
+ if reference is None:
+ reference = metrics
+ assert metrics == reference, "Parallel execution changed results"
+ item = {
+ "workers": workers,
+ "runs": args.jobs,
+ "wall_seconds": elapsed,
+ "runs_per_second": args.jobs / elapsed,
+ "includes_pool_startup": True,
+ "includes_recorded_json": True,
+ "timings": results,
+ }
+ report["parallel"].append(item)
+ save()
+ print(
+ "Throughput",
+ json.dumps({k: v for k, v in item.items() if k != "timings"}),
+ flush=True,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/run.py b/benchmarks/run.py
new file mode 100644
index 00000000..ab4aa999
--- /dev/null
+++ b/benchmarks/run.py
@@ -0,0 +1,164 @@
+"""Reproducible backend comparison using public Vessim APIs.
+
+Run from the repository: python benchmarks/run.py --backend native --output result.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import cProfile
+import gc
+import hashlib
+import importlib.metadata
+import json
+import platform
+import statistics
+import sys
+import tempfile
+import time
+import tracemalloc
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+import numpy as np
+import pandas as pd
+from loguru import logger
+import vessim as vs
+
+CASES = ("static", "traces", "csv", "many_grids", "mixed_rates", "feedback")
+
+
+def environment_info():
+ versions = {}
+ for package in ("vessim", "numpy", "pandas", "mosaik"):
+ try:
+ versions[package] = importlib.metadata.version(package)
+ except importlib.metadata.PackageNotFoundError:
+ versions[package] = None
+ return {"python": platform.python_version(), "platform": platform.platform(), **versions}
+
+
+class Feedback(vs.Controller):
+ def start(self, environment):
+ self.grids = environment.microgrids
+
+ def step(self, now, microgrid_states):
+ for grid in self.grids.values():
+ grid.policy.charge_power = 100 if now.minute % 2 else -100
+
+
+def build(backend, case, traces, directory):
+ env = vs.Environment(sim_start="2026-01-01", step_size=60, backend=backend)
+ for i in range(10 if case == "many_grids" else 1):
+ traced = case != "static"
+ env.add_microgrid(
+ name=f"grid{i}",
+ actors=[
+ vs.Producer(
+ "solar",
+ traces["solar"] if traced else 900,
+ step_size=180 if case == "mixed_rates" else None,
+ ),
+ vs.Consumer("load", traces["load"] if traced else 600),
+ ],
+ dispatchables=[
+ vs.SimpleBattery("battery", 1500, initial_soc=0.5, min_soc=0.1),
+ vs.GridConnection(),
+ ],
+ grid_signals={"carbon": traces["carbon"]} if traced else None,
+ )
+ if case == "feedback":
+ env.add_controller(Feedback())
+ memory = vs.Recorder(metrics=False)
+ env.add_controller(memory)
+ if case == "csv":
+ env.add_controller(vs.Recorder(directory, metrics=False))
+ return env, memory
+
+
+def digest(memory):
+ rows = [(str(t), value) for t, value in memory.log.items()]
+ return hashlib.sha256(json.dumps(rows, sort_keys=True).encode()).hexdigest()
+
+
+def main():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--backend", choices=["native", "mosaik"], default="native")
+ parser.add_argument("--steps", type=int, default=1000)
+ parser.add_argument("--repeats", type=int, default=3)
+ parser.add_argument("--output", type=Path, required=True)
+ parser.add_argument("--profile", type=Path, help="Directory for per-workload cProfile files")
+ args = parser.parse_args()
+ if args.steps < 1 or args.repeats < 1:
+ parser.error("steps and repeats must be positive")
+ logger.remove()
+ results = {
+ "backend": args.backend,
+ **environment_info(),
+ "cases": {},
+ }
+ with tempfile.TemporaryDirectory() as tmp:
+ directory = Path(tmp)
+ if args.steps == 1000:
+ (directory / "input.csv").write_bytes(
+ Path(__file__).with_name("input.csv").read_bytes()
+ )
+ else:
+ offsets = np.arange(args.steps + 1) * 60
+ pd.DataFrame(
+ {
+ "solar": 1200 * np.maximum(0, np.sin(offsets / 86400 * 2 * np.pi)),
+ "load": 600 + 100 * np.cos(offsets / 1800),
+ "carbon": 300 + 100 * np.sin(offsets / 3600),
+ },
+ index=offsets,
+ ).to_csv(directory / "input.csv")
+ t0 = time.perf_counter()
+ traces = {
+ c: vs.Trace.from_csv(directory / "input.csv", column=c)
+ for c in ("solar", "load", "carbon")
+ }
+ results["trace_load_seconds"] = time.perf_counter() - t0
+ for case in CASES:
+ steps = max(1, args.steps // 5) if case == "many_grids" else args.steps
+ timings, setups = [], []
+ checksums = set()
+ for repeat in range(args.repeats):
+ gc.collect()
+ t0 = time.perf_counter()
+ env, memory = build(args.backend, case, traces, directory / f"{case}-{repeat}")
+ setups.append(time.perf_counter() - t0)
+ t0 = time.perf_counter()
+ env.run(until=steps * 60, print_progress=False)
+ timings.append(time.perf_counter() - t0)
+ checksum = digest(memory)
+ checksums.add(checksum)
+ if len(checksums) != 1:
+ raise RuntimeError(f"Repeated {case} runs produced different trajectories")
+ gc.collect()
+ tracemalloc.start()
+ env, memory = build(args.backend, case, traces, directory / f"{case}-memory")
+ env.run(until=steps * 60, print_progress=False)
+ _, peak = tracemalloc.get_traced_memory()
+ tracemalloc.stop()
+ results["cases"][case] = {
+ "steps": steps,
+ "run_seconds": statistics.median(timings),
+ "setup_seconds": statistics.median(setups),
+ "runs": timings,
+ "peak_traced_bytes": peak,
+ "trajectory_sha256": checksum,
+ }
+ print(case, results["cases"][case], flush=True)
+ if args.profile:
+ args.profile.mkdir(parents=True, exist_ok=True)
+ env, _ = build(args.backend, case, traces, directory / f"{case}-profile")
+ profiler = cProfile.Profile()
+ profiler.runcall(env.run, until=steps * 60, print_progress=False)
+ profiler.dump_stats(str(args.profile / f"{case}.prof"))
+ args.output.parent.mkdir(parents=True, exist_ok=True)
+ args.output.write_text(json.dumps(results, indent=2) + "\n")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/studies.py b/benchmarks/studies.py
new file mode 100644
index 00000000..9d71f595
--- /dev/null
+++ b/benchmarks/studies.py
@@ -0,0 +1,79 @@
+"""Measure multi-run throughput and bounded versus trajectory result retention."""
+
+from __future__ import annotations
+
+import argparse
+import gc
+import json
+import sys
+import time
+import tracemalloc
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+import pandas as pd
+from loguru import logger
+import vessim as vs
+from benchmarks.run import environment_info
+
+
+def scenario(trace, steps, backend, output):
+ env = vs.Environment("2026-01-01", step_size=60, backend=backend)
+ env.add_microgrid(
+ [vs.Consumer("load", signal=trace)],
+ [vs.SimpleBattery("battery", 1500, initial_soc=0.5), vs.GridConnection()],
+ name="site",
+ )
+ if output == "metrics":
+ env.add_controller(vs.Recorder(timeseries=False))
+ elif output == "trajectory":
+ env.add_controller(vs.Recorder())
+ env.run(steps * 60)
+ return env
+
+
+def main():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--backend", choices=["native", "mosaik"], default="native")
+ parser.add_argument("--runs", type=int, default=100)
+ parser.add_argument("--steps", type=int, default=288)
+ parser.add_argument("--memory-steps", nargs="+", type=int, default=[1000, 10000])
+ parser.add_argument("--output", type=Path, required=True)
+ args = parser.parse_args()
+ if args.runs < 1 or args.steps < 1 or any(n < 1 for n in args.memory_steps):
+ parser.error("runs, steps, and memory-steps must be positive")
+ logger.remove()
+ data = pd.Series([500, 700, 600], index=[0, 3600, 7200])
+ horizon = max(args.steps, *args.memory_steps) * 60
+ if horizon > data.index[-1]:
+ data.loc[horizon] = data.iloc[-1]
+ trace = vs.Trace(data)
+ result = {
+ **environment_info(),
+ "backend": args.backend,
+ "runs": args.runs,
+ "steps_per_run": args.steps,
+ }
+ scenario(trace, 1, args.backend, "metrics") # Optional import warm-up.
+ t0 = time.perf_counter()
+ for _ in range(args.runs):
+ scenario(trace, args.steps, args.backend, "metrics")
+ result["total_seconds"] = time.perf_counter() - t0
+ result["scenarios_per_second"] = args.runs / result["total_seconds"]
+ result["memory"] = []
+ for output in ("none", "metrics", "trajectory"):
+ for steps in args.memory_steps:
+ gc.collect()
+ tracemalloc.start()
+ env = scenario(trace, steps, args.backend, output)
+ _, peak = tracemalloc.get_traced_memory()
+ tracemalloc.stop()
+ result["memory"].append({"output": output, "steps": steps, "peak_traced_bytes": peak})
+ del env
+ args.output.parent.mkdir(parents=True, exist_ok=True)
+ args.output.write_text(json.dumps(result, indent=2) + "\n")
+ print(json.dumps(result, indent=2))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/datasets/replay_example.csv b/datasets/replay_example.csv
new file mode 100644
index 00000000..d0ad05c3
--- /dev/null
+++ b/datasets/replay_example.csv
@@ -0,0 +1,8 @@
+seconds,load,solar
+0,700,0
+3600,700,1200
+7200,700,2400
+10800,700,1200
+14400,700,0
+18000,700,0
+21600,700,0
diff --git a/docs/about.md b/docs/about.md
index b4910aa2..78c85df4 100644
--- a/docs/about.md
+++ b/docs/about.md
@@ -1,21 +1,21 @@
# About Vessim
-Research on microgrids and energy management, and in particular on energy- and carbon-aware computing where Vessim originated, often relies on simulation, as physical testbeds are rare and costly.
-Vessim addresses this gap by simplifying and unifying simulation-based evaluations and enabling continuous testing of emerging applications.
+Vessim is developed by [Philipp Wiesner](https://philippwiesner.org) at the [University of Cambridge](https://www.cam.ac.uk/) in the [Machine Learning Systems Lab](https://mlsys.cst.cam.ac.uk/).
+For feedback, ideas, or questions, feel free to post on [GitHub Discussions](https://github.com/dos-group/vessim/discussions).
+If you’re interested in collaborating or want advice on using Vessim in a commercial project, drop me an email at _pw560 at cam.ac.uk_.
-Vessim is developed by [Philipp Wiesner](https://philippwiesner.org) at the [Distributed and Operating Systems (DOS)](https://www.dos.tu-berlin.de) group at TU Berlin.
-Active development for Vessim began in March 2023 with initial funding from the [German Federal Ministry of Education and Research (BMBF)](https://www.bmbf.de/) as part of the Software Campus project [SYNERGY](https://softwarecampus.de/en/projekt/synergy-synergies-of-distributed-artificial-intelligence-and-renewable-energy-generation/).
-We thank everyone who has contributed to Vessim over the past years, especially Marvin Steinke, Paul Kilian, and Amanda Malkowski.
+Development of Vessim began in March 2023 at the [Distributed and Operating Systems (DOS)](https://www.dos.tu-berlin.de) group at TU Berlin, with initial funding from the [German Federal Ministry of Education and Research (BMBF)](https://www.bmbf.de/) as part of the Software Campus project [SYNERGY](https://softwarecampus.de/en/projekt/synergy-synergies-of-distributed-artificial-intelligence-and-renewable-energy-generation/).
+
+We thank everyone who has contributed to Vessim over the past years, especially Marvin Steinke, Paul Kilian, Amanda Malkowski, and Julius Irion.
## Roadmap
We are currently working on the following aspects and features:
-- Datasets: We are working on better integrating exiting data sources like electricity prices, carbon intensity data, or weather forecasts into Vessim.
+- Probabilistic modelling: Better support for Monte Carlo simulation runs with inputs sampled from probability distributions.
+- Datasets: We are working on better integrating existing data sources like electricity prices, carbon intensity data, or weather forecasts into Vessim.
- System Advisor Model (SAM): We are working on integrating NREL's [SAM](https://sam.nrel.gov/) as a subsystem in Vessim, allowing for better simulation of solar arrays, wind farms, and other types of renewable energy generators.
- Battery degradation: We are working on integrating NREL's [BLAST-Lite](https://github.com/NREL/BLAST-Lite) for modeling battery lifetime and degradation
-- Improved modeling: We are working on more sophisticated modeling of e.g. demand response signals, time-varying PUE, ...
-- Calibration: We are working on a methodology for calibrating Vessim simulations on real hardware testbeds.
## License
diff --git a/docs/api_reference/actor.md b/docs/api_reference/actor.md
index 58cf32fc..b00adf50 100644
--- a/docs/api_reference/actor.md
+++ b/docs/api_reference/actor.md
@@ -1,3 +1,14 @@
-# Actor
+# Actors
+
+[`Consumer`](#vessim.Consumer) models demand, [`Producer`](#vessim.Producer) models generation, and [`Actor`](#vessim.Actor) supports signed inputs that can change direction.
+See [Actors and signals](../concepts/signals.md) for examples.
+
+::: vessim.Consumer
+ options:
+ inherited_members: [signal]
+
+::: vessim.Producer
+ options:
+ inherited_members: [signal]
::: vessim.Actor
diff --git a/docs/api_reference/cli.md b/docs/api_reference/cli.md
deleted file mode 100644
index b0f01fee..00000000
--- a/docs/api_reference/cli.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Command-Line Interface
-
-Vessim ships with a small CLI for inspecting experiment results in the browser.
-
-## `vessim view`
-
-Launches the experiment viewer on a results directory written by [`CsvLogger`](controller.md).
-
-```
-usage: vessim view [-h] [-p PORT] [--no-browser] directory
-
-positional arguments:
- directory Path to a results directory
-
-options:
- -p PORT, --port PORT Port to serve on (default: 8710)
- --no-browser Do not open the browser automatically
-```
-
-### Single-experiment mode
-
-Pass a directory that contains a `metadata.yaml` and `timeseries.csv` directly. The viewer opens that single experiment:
-
-```console
-vessim view results/basic_example
-```
-
-### Multi-experiment mode
-
-Pass a parent directory that contains several experiment subdirectories. The viewer adds a sidebar from which you can browse runs:
-
-```console
-vessim view results/
-```
diff --git a/docs/api_reference/controller.md b/docs/api_reference/controller.md
index 255be86c..c8ae7509 100644
--- a/docs/api_reference/controller.md
+++ b/docs/api_reference/controller.md
@@ -1,10 +1,20 @@
-# Controller
+# Controllers
-Controllers run at every simulation step.
+Controllers run after dispatch at each simulation step.
They can observe the state of all microgrids and modify their components.
See the [Controllers](../concepts/controllers.md) concept page for usage examples.
::: vessim.Controller
-::: vessim.CsvLogger
-::: vessim.MemoryLogger
+
::: vessim.Api
+
+### REST API commands
+
+Install `vessim[live]` to use `Api`.
+Its running server exposes interactive endpoint documentation at `http://localhost:8700/docs` with the default port.
+
+API commands are acknowledged when queued.
+Changes appear in observations after the command is applied and the affected component is sampled again.
+Assigning an actor's `signal` through the API replaces its input with a static signal.
+
+For recording and custom metric APIs, see [Recording and metrics](recorder.md).
diff --git a/docs/api_reference/dispatch_policy.md b/docs/api_reference/dispatch_policy.md
index ffa9c9d3..23fea286 100644
--- a/docs/api_reference/dispatch_policy.md
+++ b/docs/api_reference/dispatch_policy.md
@@ -1,11 +1,10 @@
-# Dispatch Policy
+# Dispatch policy
-A dispatch policy receives the microgrid's current power delta and allocates it
-across [Dispatchables](dispatchable.md), returning the remainder to be exchanged
-with the public grid. Subclass [`DispatchPolicy`](#vessim.DispatchPolicy) to
-implement custom strategies. See the
-[Dispatchables and Dispatch Policies](../concepts/dispatchables.md) concept page
-for usage examples.
+A dispatch policy allocates the microgrid's power surplus or deficit across [dispatchables](dispatchable.md).
+Each `apply()` call assigns feasible resource setpoints and returns `None`.
+The microgrid validates bounds and power balance.
+
+See [Dispatchables and policies](../concepts/dispatchables.md) for examples.
-::: vessim.DispatchPolicy
::: vessim.DefaultDispatchPolicy
+::: vessim.DispatchPolicy
diff --git a/docs/api_reference/dispatchable.md b/docs/api_reference/dispatchable.md
index 769cf0a9..e219cd89 100644
--- a/docs/api_reference/dispatchable.md
+++ b/docs/api_reference/dispatchable.md
@@ -1,12 +1,21 @@
-# Dispatchable
+# Dispatchables
-Controllable energy resources whose power is allocated by a
-[Dispatch Policy](dispatch_policy.md). Subclass [`Dispatchable`](#vessim.Dispatchable)
-to model any device with a controllable power setpoint. See the
-[Dispatchables and Dispatch Policies](../concepts/dispatchables.md) concept page for
-usage examples and a guide to writing custom dispatchables.
+A [dispatch policy](dispatch_policy.md) assigns power to these resources.
+Use the built-in grid and battery models, or subclass `Dispatchable` for a custom resource.
+See [Dispatchables and policies](../concepts/dispatchables.md) for examples.
-::: vessim.Dispatchable
-::: vessim.Storage
+Positive power means absorption or export.
+Negative power means supply or import.
+Battery observations include `current_power` in W, `charge_level` in Wh, and `soc` between 0 and 1.
+
+::: vessim.GridConnection
+ options:
+ inherited_members: [set_power]
::: vessim.SimpleBattery
+ options:
+ inherited_members: [set_power]
::: vessim.ClcBattery
+ options:
+ inherited_members: [set_power]
+::: vessim.Dispatchable
+::: vessim.Storage
diff --git a/docs/api_reference/environment.md b/docs/api_reference/environment.md
index 6defb6f2..7991590b 100644
--- a/docs/api_reference/environment.md
+++ b/docs/api_reference/environment.md
@@ -1 +1,32 @@
+# Environment and RunResult
+
+Create and run simulations with `Environment`.
+Its `run()` method returns a `RunResult` containing the final state.
+See [Getting Started](../getting_started.md#run-your-first-simulation) for a walkthrough or [Connect External Simulators](../concepts/coupling.md) for Mosaik.
+
::: vessim.Environment
+
+::: vessim.RunResult
+
+## Time and final state
+
+Actor sampling intervals must be whole multiples of the environment's `step_size`.
+Inputs are held between samples.
+Controller changes to an actor's input take effect at its next sample, while changes to policy parameters affect the next dispatch.
+
+Ticks occur strictly before `until`.
+The final integration interval is shortened as needed to reach that horizon.
+`RunResult.states` contains the final resource state, while `last_time` and recorded observations refer to the last tick.
+Energy metrics include the final interval.
+
+## Mosaik connections
+
+With `backend="mosaik"`, `env.world` exposes the Mosaik world.
+Call `env.prepare()` before connecting to microgrids through `env.entities[name]`, then use `env.run()` to execute and clean up the coupled simulation.
+
+The microgrid input `power` is in W, with generation positive and consumption negative.
+Multiple external inputs are summed with local actor power.
+A microgrid fed entirely by external simulators may have no actors, but still needs dispatchables to balance its power.
+
+Available outputs are `p_delta`, `p_grid`, `actor_states`, `dispatch_states`, `policy_state`, and `grid_signals`.
+Vessim uses integer-second scheduling with Mosaik's `time_resolution=1.0`.
diff --git a/docs/api_reference/microgrid.md b/docs/api_reference/microgrid.md
index 9b937a2c..fd10e2a2 100644
--- a/docs/api_reference/microgrid.md
+++ b/docs/api_reference/microgrid.md
@@ -1,4 +1,22 @@
# Microgrid
+Create microgrids through [`Environment.add_microgrid()`](environment.md#vessim.Environment.add_microgrid).
+Controllers receive their observations as `MicrogridState` dictionaries.
+
+## Power balance and units
+
+The sum of actor and external simulator power is `p_delta`, the surplus or deficit allocated across dispatchables.
+A grid connection must be explicitly configured.
+Unmet demand or surplus that cannot be absorbed raises an error.
+
+| Quantity | Positive | Negative |
+| --- | --- | --- |
+| Actor power and `p_delta` | Generation or surplus | Consumption or deficit |
+| Dispatchable power | Absorbing power | Supplying power |
+| Grid exchange, `p_grid` | Export | Import |
+
+Power is in W, energy and battery capacity in Wh, and state of charge ranges from 0 to 1.
+Multiple microgrids share simulation time but have independent power balances unless explicitly coupled.
+
::: vessim.Microgrid
::: vessim.MicrogridState
diff --git a/docs/api_reference/recorder.md b/docs/api_reference/recorder.md
new file mode 100644
index 00000000..30015872
--- /dev/null
+++ b/docs/api_reference/recorder.md
@@ -0,0 +1,52 @@
+# Recording and metrics
+
+See [Results and metrics](../concepts/results.md) for recording choices and custom calculations, or [Simulation studies](../concepts/studies.md) for comparing runs.
+
+::: vessim.Recorder
+::: vessim.ExperimentResult
+::: vessim.Metric
+::: vessim.ElectricityImportCost
+::: vessim.ElectricityExportRevenue
+::: vessim.GridImportEmissions
+
+## Standard metrics
+
+| Scope | Metrics | Unit |
+| --- | --- | --- |
+| Actor and microgrid | `energy_produced`, `energy_consumed` | Wh |
+| Microgrid | `grid_import`, `grid_export` | Wh |
+| Microgrid | `peak_grid_import`, `peak_grid_export` | W |
+
+Actor production and consumption are accumulated separately from sampled power.
+Microgrid production and consumption sum the actors, excluding dispatchables and external simulator inputs.
+Grid import and export integrate the net exchange of all grid connections.
+Peak values are sampled maxima, not rolling billing demand.
+
+Energy totals cover the full [simulation duration](environment.md#time-and-final-state), including a shortened final interval.
+For incomplete runs, check `execution["status"]` and the [partial-recording semantics](#partial-recordings).
+
+## Saved records
+
+An experiment uses `format="vessim.experiment"` and `schema_version=1`.
+Use `ExperimentResult.load()` and `save()` to read and write validated records.
+`to_dict()` provides a detached dictionary for other tools.
+
+`timeseries` is a relative CSV path or null.
+`timeseries_path` resolves it from the original record location.
+Saving a loaded record elsewhere copies the CSV beside the new JSON.
+Saving in-memory observations does not persist a trajectory unless the recorder was configured with an output directory.
+
+Keep JSON and CSV together when moving recordings.
+For embedded `vessim.study` records, CSV paths are relative to the study file.
+Prefer opening a collection of experiment directories in the viewer when each run has its own CSV.
+
+CSV columns are fixed by the first observation.
+Nested state fields use dot-separated paths, escaping literal `.` as `%2E` and `%` as `%25` within names.
+Changing the state schema during a run raises an error.
+
+## Partial recordings
+
+`Recorder` accumulates metrics for an interval when its starting observation is recorded.
+The interval is clipped to the requested simulation horizon.
+If a run fails or is interrupted, the last accumulated interval can extend beyond the point execution reached, particularly in live mode.
+Check `execution["status"]` before using such totals in a comparison.
diff --git a/docs/api_reference/signal.md b/docs/api_reference/signal.md
index 9b7ad670..b043047b 100644
--- a/docs/api_reference/signal.md
+++ b/docs/api_reference/signal.md
@@ -1,18 +1,35 @@
-# Signal
+# Signals
-A [`Signal`](#vessim.Signal) provides a time-varying value to actors and grid
-signals. See the [Signals and Datasets](../concepts/signals.md) concept page for
-usage examples.
+A `Signal` provides numeric input to actors or contextual data such as electricity prices.
+See [Actors and signals](../concepts/signals.md) for usage examples.
-::: vessim.Signal
::: vessim.StaticSignal
::: vessim.Trace
+::: vessim.Signal
+
+## Custom signal lifecycle
+
+The environment calls `start()` before a signal is first sampled and `finalize()` when the environment closes, including after a failure.
+Shared signals are started and finalized once per environment.
+Replaced signals remain owned until cleanup.
+
+A signal may be read by several components at the same simulation time.
+Stateful implementations should handle repeated reads consistently.
+Create fresh stateful signals for each run.
+Immutable traces can be reused, but live signals must not be shared between environments.
+
+## Live signals
-## Software-in-the-Loop signals
+These signals require the `live` extra (`pip install 'vessim[live]'`).
+See the [Live simulation](../concepts/live.md) page for setup and usage.
-These signals require the `sil` extra (`pip install vessim[sil]`). See the
-[Software-in-the-Loop](../concepts/sil.md) concept page for the full picture.
+Live signals require `Environment.live()` and poll in the background.
+They return zero until the first reading and retain the last value if polling fails.
+There is no automatic freshness check.
+Custom `_fetch_current_value()` implementations should use bounded request timeouts.
-::: vessim.SilSignal
+::: vessim.LiveSignal
+ options:
+ members: [at, start, finalize, _fetch_current_value]
::: vessim.PrometheusSignal
::: vessim.WatttimeSignal
diff --git a/docs/assets/optuna-search-results.png b/docs/assets/optuna-search-results.png
new file mode 100644
index 00000000..6778ad9c
Binary files /dev/null and b/docs/assets/optuna-search-results.png differ
diff --git a/docs/concepts/controllers.md b/docs/concepts/controllers.md
index 2a18d5fd..0439a8c1 100644
--- a/docs/concepts/controllers.md
+++ b/docs/concepts/controllers.md
@@ -1,84 +1,64 @@
# Controllers
-A [`Controller`](../api_reference/controller.md) is executed at every simulation step.
-It can observe the state of all microgrids, log it, and modify components of the running simulation, for example by changing parameters of a [`DispatchPolicy`](../api_reference/dispatch_policy.md) on the fly.
+Controllers let you observe and influence a running simulation.
+Use them to record results, connect an application, or adjust how a datacenter operates.
+A controller can access every microgrid in its environment.
-Vessim ships with two built-in loggers ([`CsvLogger`](../api_reference/controller.md#vessim.CsvLogger) and [`MemoryLogger`](../api_reference/controller.md#vessim.MemoryLogger)), an [`Api`](../api_reference/controller.md#vessim.Api) controller for software-in-the-loop experiment, and you can write your own by subclassing `Controller`.
+Register controllers with `env.add_controller()` before running the simulation.
+You can combine several, for example to record results while testing a control strategy.
-## Logging with CsvLogger
+## Recorder
-[`CsvLogger`](../api_reference/controller.md#vessim.CsvLogger) is the recommended way to record an experiment. It writes the static configuration and the full timeseries to a directory:
+[`vs.Recorder`](../api_reference/recorder.md#vessim.Recorder) collects time series and calculates metrics such as grid imports and exports:
```python
-environment.add_controller(vs.CsvLogger("results/my_experiment"))
+recorder = env.add_controller(vs.Recorder("results/experiment"))
```
-After the run, the directory contains:
+See [Results and metrics](results.md) to inspect recordings and add your own calculations.
-```
-results/my_experiment/
- metadata.yaml # static experiment configuration
- timeseries.csv # power flows, battery state, grid signals at every step
-```
-
-These two files are exactly what the [experiment viewer](../getting_started.md#7-exploring-the-results) consumes. Open them in your browser with `vessim view results/my_experiment`.
-
-!!! tip "MemoryLogger"
- For interactive exploration in a notebook, use [`vs.MemoryLogger()`](../api_reference/controller.md#vessim.MemoryLogger) instead. It keeps the simulation state in memory and exposes `.to_df()` for direct DataFrame analysis.
-
-## Writing a custom controller
-
-Subclass [`Controller`](../api_reference/controller.md) and implement `step`. Optionally override `start` to grab references to the environment before the simulation begins:
-
-```python
-import vessim as vs
+## Api
-class MyController(vs.Controller):
- def start(self, environment):
- # Called once before the simulation starts
- self.microgrids = environment.microgrids
+[`vs.Api`](../api_reference/controller.md#vessim.Api) lets an external application read simulation state and send commands through a REST API.
+For example, a job scheduler can observe available solar power and adjust datacenter demand.
- def step(self, now, microgrid_states):
- # Called at every simulation step
- ...
-```
-
-The `step` method receives:
-
-- `now` — the current simulation time as a `datetime`.
-- `microgrid_states` — a `dict[str, MicrogridState]` keyed by microgrid name. Each [`MicrogridState`](../api_reference/microgrid.md#vessim.MicrogridState) exposes `p_delta`, `p_grid`, `actor_states`, `dispatch_states`, `policy_state`, and `grid_signals`.
-
-## Example: a scheduled charger
+Use it with `vs.Environment.live()` to test the application against a simulation running in real time.
+[Live simulation](live.md#connect-an-application) walks through the setup and requests.
-Cheap and clean grid hours are typically at night. Let's force-charge the battery between midnight and 6 AM by mutating the `charge_power` of the dispatch policy at runtime:
+## Custom controllers
-```python
-import vessim as vs
-
-class ScheduledCharger(vs.Controller):
- """Force-charges storage at 500 W between 00:00 and 06:00."""
+Subclass [`vs.Controller`](../api_reference/controller.md#vessim.Controller) and implement `step()` to respond to observations.
+Vessim calls it after each dispatch.
+Changes to model inputs or parameters affect subsequent steps.
- def __init__(self, microgrid_name: str):
- self.microgrid_name = microgrid_name
+### Demand response
- def start(self, environment):
- mg = environment.microgrids[self.microgrid_name]
- assert isinstance(mg.policy, vs.DefaultDispatchPolicy)
- self.policy = mg.policy
-
- def step(self, now, microgrid_states):
- self.policy.charge_power = 500.0 if now.hour < 6 else 0.0
-```
+Consider a datacenter with a flexible connection agreement.
+In this scenario, the grid operator can temporarily lower its import limit with advance notice.
+The datacenter responds by pausing flexible workloads until more power is available.
-Add it to the environment alongside the logger:
+The controller passes the announced limit to a datacenter simulator, whose `set_power_budget()` method adjusts workload concurrency.
+Its power consumption feeds a `vs.Consumer` through a [custom signal](signals.md#custom-signals).
```python
-environment.add_controller(ScheduledCharger("datacenter"))
-environment.add_controller(vs.CsvLogger("results/scheduled"))
+class DemandResponse(vs.Controller):
+ def __init__(self, datacenter, grid):
+ self.datacenter = datacenter
+ self.grid = grid
+
+ def step(self, now, states):
+ limit = states["site"]["grid_signals"]["next_import_limit"]
+ self.datacenter.set_power_budget(limit)
+ self.grid.max_import = limit
```
-The same pattern works for any policy parameter: switch between policies, raise/lower a battery's `min_soc`, or react to grid signals exposed via `microgrid_states[name]["grid_signals"]`.
+Here, each notice applies to the next simulation step, when both the reduced workload and the new connection limit take effect.
+When the limit rises again, the simulator resumes more workloads.
+`set_power_budget()` belongs to your datacenter model, so you decide how it responds and which workloads can wait.
-## Going further: real-time control
+A controller changes how the system operates.
+The [dispatch policy](dispatchables.md#dispatch-policies) then allocates resource power to serve the resulting demand.
+Use a policy to choose battery or generator power, and a controller to adjust workload or coordinate several sites.
-Custom controllers can also expose the simulation to the outside world. The built-in [`Api`](../api_reference/controller.md#vessim.Api) controller starts a REST server that lets external programs query the state and send control commands while the simulation runs. See [Software-in-the-Loop](sil.md) for the full picture.
+The [complete example](https://github.com/dos-group/vessim/blob/main/examples/demand_response.py) connects a simple datacenter model and runs through a temporary restriction.
+See the [Controller API](../api_reference/controller.md) for initialization and cleanup hooks.
diff --git a/docs/concepts/coupling.md b/docs/concepts/coupling.md
new file mode 100644
index 00000000..ec14cc6e
--- /dev/null
+++ b/docs/concepts/coupling.md
@@ -0,0 +1,55 @@
+# Connect External Simulators
+
+Vessim can connect domain-specific simulators written in any programming language, including simulators running on other computers, through its Mosaik backend.
+[Mosaik](https://mosaik.offis.de/) is a co-simulation framework that coordinates simulation time and exchanges data between models.
+Each simulator implements Mosaik's interface so it can participate in a shared simulation.
+
+For example, a datacenter simulator can model job scheduling and server power while Vessim models its electricity supply.
+You can reuse that simulator and explore how its workloads interact with batteries, renewable generation, and grid constraints.
+
+## Connect a datacenter model
+
+Install the backend:
+
+```console
+python -m pip install 'vessim[mosaik]'
+```
+
+The [complete example](https://github.com/dos-group/vessim/blob/main/examples/external_simulator_coupling.py) provides a small datacenter simulator whose demand increases as jobs arrive.
+Download it as `external_simulator_coupling.py`.
+With that file alongside your script, the connection looks like this:
+
+```python
+import vessim as vs
+
+with vs.Environment(
+ "2026-01-01",
+ step_size=60,
+ backend="mosaik",
+ sim_config={
+ "Datacenter": {"python": "external_simulator_coupling:DatacenterSimulator"},
+ },
+) as env:
+ datacenter = env.world.start("Datacenter").Datacenter()
+ env.add_microgrid(name="site", actors=[], dispatchables=[vs.GridConnection()])
+ recorder = env.add_controller(vs.Recorder())
+
+ env.prepare()
+ env.world.connect(datacenter, env.entities["site"], "power")
+ env.run(180)
+```
+
+Here, the external simulator supplies the microgrid's demand.
+`env.prepare()` makes the microgrid available to Mosaik, and `connect()` links the datacenter's power output to it.
+When the environment runs, Mosaik advances both simulators and passes the demand to Vessim at each step.
+Inspect the recording as usual with `recorder.to_df()` or [calculate metrics](results.md).
+
+## Connect your own simulator
+
+Wrap your model with the [Mosaik simulator API](https://mosaik.readthedocs.io/en/3.6.0/mosaik-api/), or use an existing Mosaik-compatible simulator.
+The example loads a Python class locally.
+Mosaik can also start a separate process or connect to a simulator already running on another computer through its [simulator configuration](https://mosaik.readthedocs.io/en/3.6.0/simmanager.html).
+
+You can also send Vessim observations back to another simulator, for example to let a scheduler respond to battery charge.
+See Mosaik's [feedback connections](https://mosaik.readthedocs.io/en/3.6.0/scenario-definition.html#how-to-achieve-cyclic-data-flows) and the [Vessim connection reference](../api_reference/environment.md#mosaik-connections) for the interface details.
+Use `vs.Environment.live(backend="mosaik", ...)` when the coupled simulation should run in real time.
diff --git a/docs/concepts/dispatchables.md b/docs/concepts/dispatchables.md
index ee2d3fd0..d39992f7 100644
--- a/docs/concepts/dispatchables.md
+++ b/docs/concepts/dispatchables.md
@@ -1,170 +1,162 @@
-# Dispatchables and Dispatch Policies
+# Dispatchables and policies
-A [`Dispatchable`](../api_reference/dispatchable.md) is a controllable energy resource (a battery, diesel/gas generator, or electrolyzer) whose power setpoint is decided each step by a [`DispatchPolicy`](../api_reference/dispatch_policy.md).
-Unlike actors (whose power comes from a signal), a dispatchable's power is allocated by the policy from the microgrid's current power delta.
+Dispatchables are resources whose power is chosen by a dispatch policy, such as batteries, generators, and grid connections.
+Each microgrid can contain any combination of them.
+At each step, the policy decides how to use these resources to handle the actors' combined power surplus or deficit.
-Each `Dispatchable` reports a *feasible range* `(min_power, max_power)` for the upcoming timestep. Negative values represent discharging/generation; positive values represent charging/consumption.
+## Dispatchables
-## Battery models
+A dispatchable can:
-### SimpleBattery
+- **Absorb surplus power**, for example a dump load or a mechanism for accounting for curtailed generation.
+- **Supply missing power**, for example a diesel or gas generator.
+- **Do both**, for example a battery or a grid connection.
-[`SimpleBattery`](../api_reference/dispatchable.md#vessim.SimpleBattery) is an ideal, capacity-based model which is fast and easy to parameterize. It is a sensible default for simple simulations:
+Each resource defines its operating constraints.
+Batteries have capacity and charge-rate limits, grid connections can limit power and ramp rates, and a generator model may include startup delays or minimum output.
-```python
-import vessim as vs
-
-battery = vs.SimpleBattery(
- name="battery",
- capacity=5000, # Wh (total energy capacity
- initial_soc=0.8, # 80 % charged at simulation start
- min_soc=0.2, # never discharge below 20 % SoC
- c_rate=0.5, # optional: cap charge/discharge rate at 0.5C
-)
-```
+### Batteries
-`c_rate` is the maximum charge/discharge rate as a fraction of capacity per hour. A 1 kWh battery with `c_rate=0.5` can charge or discharge at most 500 W. If omitted, the only limit is the available energy.
-
-### ClcBattery
-
-[`ClcBattery`](../api_reference/dispatchable.md#vessim.ClcBattery) implements the C-L-C model for lithium-ion batteries ([Kazhamiaka et al., 2019](https://doi.org/10.1186/s42162-019-0070-6)). It captures realistic degradation of charge/discharge limits as a function of current and is appropriate for high-fidelity studies. The default parameterization models a pack of LGM50 21700 Li-ion cells:
+[`vs.SimpleBattery`](../api_reference/dispatchable.md#vessim.SimpleBattery) stores surplus energy and supplies it later.
+This creates a 1 kWh battery, initially half full, with a charge and discharge limit of 500 W:
```python
-battery = vs.ClcBattery(
- name="battery",
- number_of_cells=100,
- initial_soc=0.5,
- min_soc=0.1,
-)
+import vessim as vs
+
+battery = vs.SimpleBattery("battery", capacity=1000, initial_soc=0.5, c_rate=0.5)
```
-!!! note
- `ClcBattery` should not be used with large simulation step sizes. For coarse-grained simulations (15 min or more), prefer `SimpleBattery`.
+The simple model assumes ideal charging and discharging.
+[`vs.ClcBattery`](../api_reference/dispatchable.md#vessim.ClcBattery) adds conversion losses and current-dependent limits for more detailed battery simulations.
-## Multiple dispatchables
+### Grid connections
-[`add_microgrid`](../api_reference/environment.md#vessim.Environment.add_microgrid) accepts a list of dispatchables. The default policy serves them in order, so the first one is used until it is exhausted before the next one is touched:
+[`vs.GridConnection`](../api_reference/dispatchable.md#vessim.GridConnection) imports power to meet demand and exports surplus.
+For example, limit imports to 5 kW and exports to 2 kW:
```python
-environment.add_microgrid(
- name="datacenter",
- actors=[...],
- dispatchables=[
- vs.SimpleBattery(name="primary", capacity=10000, initial_soc=0.9),
- vs.SimpleBattery(name="backup", capacity=5000, initial_soc=0.5),
- ],
-)
+grid = vs.GridConnection("grid", max_import=5000, max_export=2000)
```
-## Dispatch policies
-
-A [`DispatchPolicy`](../api_reference/dispatch_policy.md) receives the microgrid's power delta and allocates it across dispatchables. It returns the power that needs to be exchanged with the public grid.
+Optional `ramp_up` and `ramp_down` constrain changes in power, measured in W per simulation step.
+A [controller](controllers.md) can set `grid.connected = False` to simulate an outage and `True` to reconnect.
+Omit the grid connection to model an islanded system.
-### DefaultDispatchPolicy
+### Custom dispatchables
-[`DefaultDispatchPolicy`](../api_reference/dispatch_policy.md#vessim.DefaultDispatchPolicy) is used when no `policy` argument is given to `add_microgrid`. It supports three modes:
+Subclass [`vs.Dispatchable`](../api_reference/dispatchable.md#vessim.Dispatchable) to model other resources, such as diesel or gas generators.
+A custom resource can also account for curtailed generation as an absorption of surplus power.
-Grid-connected (default): allocate the delta in priority order, exchange the remainder with the grid.
+This example models a running gas turbine with minimum and maximum output and a ramp limit.
+The illustrative turbine supplies between 1 and 5 MW and can change its output by up to 0.5 MW per simulation step:
```python
-policy = vs.DefaultDispatchPolicy(mode="grid-connected")
-```
+class GasTurbine(vs.Dispatchable):
+ def __init__(self, name, min_output, max_output, ramp_limit):
+ super().__init__(name)
+ self.min_output = min_output
+ self.max_output = max_output
+ self.ramp_limit = ramp_limit
+ self.output = min_output
+ self.current_power = -min_output
+
+ def feasible_range(self, duration):
+ lower = max(self.min_output, self.output - self.ramp_limit)
+ upper = min(self.max_output, self.output + self.ramp_limit)
+ return -upper, -lower # Generation uses negative dispatchable power.
+
+ def step(self, duration):
+ self.output = -self.current_power
+
+ def config(self):
+ return {
+ "min_output": self.min_output,
+ "max_output": self.max_output,
+ "ramp_limit": self.ramp_limit,
+ }
+
+ def state(self):
+ return {"current_power": self.current_power}
-Islanded: fully disconnect the microgrid from the grid. If dispatchables cannot cover a deficit, a `RuntimeError` is raised. Use this to verify a self-sufficient design.
-```python
-policy = vs.DefaultDispatchPolicy(mode="islanded")
+turbine = GasTurbine("gas_turbine", min_output=1e6, max_output=5e6, ramp_limit=0.5e6)
+dispatchables = [turbine, vs.GridConnection()]
```
-Fixed charge power: force storage to charge or discharge at a constant rate, balancing the difference with the grid. Useful for scheduled charging strategies; only works in grid-connected mode.
+`feasible_range()` combines the output and ramp constraints.
+`step()` remembers the dispatched output for the next step.
+`config()` and `state()` expose model parameters and observations for recording.
-```python
-policy = vs.DefaultDispatchPolicy(charge_power=200.0) # charge at 200 W
-policy = vs.DefaultDispatchPolicy(charge_power=-500.0) # discharge at 500 W
-```
+The turbine starts at its minimum output and stays running.
+If demand falls below what it can supply, the grid exports the excess.
+Startup, shutdown, and fuel consumption would require extending the model.
+
+## Dispatch policies
-## Custom dispatchables
+A policy receives the actors' net power, called `p_delta`, and the microgrid's set of dispatchables.
+A positive delta is surplus generation, and a negative delta is unmet demand.
+The policy assigns power to the resources within their operating constraints.
-To model hardware that does not fit a battery model, subclass [`Dispatchable`](../api_reference/dispatchable.md) and implement four methods:
+**After dispatch, the remaining power imbalance must be zero.**
+If a surplus or deficit remains, the dispatch is infeasible.
+Vessim raises an error and records the run as failed.
-| Method | Description |
-|---|---|
-| `feasible_range(duration)` | Return `(min_power, max_power)` achievable for the timestep. |
-| `step(duration)` | Advance internal state after `current_power` has been set. |
-| `config()` | Return static parameters (logged once to `metadata.yaml`). |
-| `state()` | Return dynamic state (logged each step to `timeseries.csv`). |
+### Default policy
-A diesel generator is stateless and only generates (negative power):
+[`vs.DefaultDispatchPolicy`](../api_reference/dispatch_policy.md#vessim.DefaultDispatchPolicy) uses resources in list order while respecting their constraints.
+For example:
```python
-class DieselGenerator(vs.Dispatchable):
- def __init__(self, name: str, max_power_w: float):
- super().__init__(name)
- self.max_power_w = max_power_w
+dispatchables = [battery, grid]
+```
- def feasible_range(self, duration: int) -> tuple[float, float]:
- return (-self.max_power_w, 0.0) # negative = generation
+This uses the battery first and exchanges the remaining power with the grid.
+Reversing the order prioritizes grid exchange.
+Add a generator or curtailment resource to the list when the system needs those options.
- def step(self, duration: int) -> None:
- pass # no internal state
+The default policy is used automatically.
+To request a fixed battery charging power, pass `policy=vs.DefaultDispatchPolicy(charge_power=200)` to `env.add_microgrid()`.
+The policy balances this target using available generation and the other resources.
- def config(self) -> dict:
- return {"max_power_w": self.max_power_w}
+### Custom policies
- def state(self) -> dict:
- return {"current_power": self.current_power}
-```
+Choosing when to charge, generate, import, or export is often the main control problem in an energy system.
+Policies can range from a simple priority order to forecast-based optimization or reinforcement learning.
+Vessim lets you test your own strategies across demand, weather, and price scenarios through [simulation studies](studies.md), including probabilistic inputs.
-Combined with a battery, the default policy will exhaust the battery first and only call on the generator for the remaining deficit:
+For the gas turbine and grid connection above, prioritize the cheaper source.
+This policy compares the current electricity price with an assumed turbine cost per kWh, then lets the default policy allocate power in that order:
```python
-environment.add_microgrid(
- name="datacenter",
- actors=[...],
- dispatchables=[
- vs.SimpleBattery(name="battery", capacity=10000, initial_soc=0.9),
- DieselGenerator(name="generator", max_power_w=5000),
- ],
-)
+class CostBasedPolicy(vs.DefaultDispatchPolicy):
+ def __init__(self, turbine_cost):
+ super().__init__()
+ self.turbine_cost = turbine_cost
+
+ def apply(self, p_delta, duration, dispatchables, grid_signals=None):
+ grid_price = grid_signals["price"]
+ ordered = sorted(
+ dispatchables,
+ key=lambda resource: (
+ grid_price if isinstance(resource, vs.GridConnection)
+ else self.turbine_cost
+ ),
+ )
+ super().apply(p_delta, duration, ordered, grid_signals)
+
+ def state(self):
+ return {"turbine_cost": self.turbine_cost}
```
-## Custom dispatch policies
+Pass `policy=CostBasedPolicy(turbine_cost=0.15)` and `grid_signals={"price": 0.10}` to `env.add_microgrid()`, using EUR/kWh for both.
+The grid is preferred at this price.
+If the price rises above 0.15, the turbine is preferred.
+Use a price trace to vary it during the run.
-For advanced energy management, subclass [`DispatchPolicy`](../api_reference/dispatch_policy.md) and implement `apply`. The microgrid's grid signals are passed in directly, so the policy can be carbon- or price-aware without extra wiring:
+The turbine's minimum output and ramp limits still apply, so the grid supplies any remaining demand or receives excess generation.
+This policy covers a turbine and a grid connection.
+To add other resource types, define their costs or priority in the ordering rule.
-```python
-class GreenChargePolicy(vs.DispatchPolicy):
- """Only charge batteries when grid carbon intensity is low."""
+For a different allocation algorithm, implement [`vs.DispatchPolicy.apply()`](../api_reference/dispatch_policy.md#vessim.DispatchPolicy.apply) and assign resource power through `set_power()`, ensuring the final balance is zero.
- def __init__(self, carbon_threshold: float):
- self.carbon_threshold = carbon_threshold
-
- def apply(self, p_delta, duration, dispatchables, grid_signals=None):
- remaining = p_delta
- carbon = (grid_signals or {}).get("carbon_intensity")
-
- for d in dispatchables:
- lo, hi = d.feasible_range(duration)
- if remaining > 0:
- # Excess: only charge if grid is clean
- if carbon is not None and carbon > self.carbon_threshold:
- d.set_power(0.0, duration)
- continue
- allocated = min(hi, remaining)
- else:
- # Deficit: always discharge
- allocated = max(lo, remaining)
- d.set_power(allocated, duration)
- remaining -= allocated
-
- return remaining # remainder goes to the grid
-
-
-environment.add_microgrid(
- name="datacenter",
- actors=[...],
- dispatchables=[vs.SimpleBattery(name="battery", capacity=5000, initial_soc=0.8)],
- policy=GreenChargePolicy(carbon_threshold=200),
- grid_signals={"carbon_intensity": vs.Trace.from_csv("datasets/watttime_example.csv")},
-)
-```
+The [strategy comparison example](https://github.com/dos-group/vessim/blob/main/examples/strategy_comparison.py) compares self-consumption, peak-shaving, and carbon-aware policies.
diff --git a/docs/concepts/live.md b/docs/concepts/live.md
new file mode 100644
index 00000000..a6bd54fe
--- /dev/null
+++ b/docs/concepts/live.md
@@ -0,0 +1,95 @@
+# Live simulation
+
+Test an application against a simulated energy system before connecting it to real equipment.
+A job scheduler, for example, can observe solar generation and battery charge, start more work when power is available, and scale back when it is scarce.
+
+`vs.Environment.live()` runs alongside real time.
+Your application reads state and sends commands through the REST API while Vessim simulates the response of the energy system.
+
+## Connect an application
+
+Install the live integration:
+
+```console
+python -m pip install 'vessim[live]'
+```
+
+Start with a datacenter drawing 700 W and solar producing 1000 W.
+The surplus charges its battery.
+Save this as `live_example.py` and run `python live_example.py`:
+
+```python
+import vessim as vs
+
+
+def main():
+ with vs.Environment.live(step_size=1) as env:
+ env.add_microgrid(
+ name="site",
+ actors=[
+ vs.Consumer("datacenter", signal=700),
+ vs.Producer("solar", signal=1000),
+ ],
+ dispatchables=[
+ vs.SimpleBattery("battery", capacity=1000, initial_soc=0.5),
+ vs.GridConnection(),
+ ],
+ )
+ env.add_controller(vs.Api(broker_port=8700))
+ env.run()
+
+
+if __name__ == "__main__":
+ main()
+```
+
+In another terminal, read the latest observation:
+
+```console
+curl http://localhost:8700/microgrids/site
+```
+
+The response includes actor power, grid exchange, and battery state of charge.
+Now represent the scheduler starting more work by raising datacenter demand to 1200 W:
+
+```console
+curl -X PUT 'http://localhost:8700/microgrids/site/actors/datacenter/signal?value=1200'
+```
+
+Demand now exceeds solar generation, so the battery starts discharging.
+Read the state again to observe the change.
+Your application can make the same requests as part of its control loop.
+The running API lists available requests at `http://localhost:8700/docs`.
+
+Use Ctrl+C to end the simulation.
+See [`vs.Api`](../api_reference/controller.md#vessim.Api) for configuration options.
+
+## Use live measurements
+
+You can also combine real equipment with simulated energy infrastructure.
+For example, feed measured server demand into Vessim using [`vs.PrometheusSignal`](../api_reference/signal.md#vessim.PrometheusSignal):
+
+```python
+power = vs.PrometheusSignal(
+ url="http://localhost:9090",
+ query="server_power_watts",
+)
+datacenter = vs.Consumer("datacenter", signal=power)
+```
+
+Use this consumer in your live environment and replace the query with a power metric from your monitoring setup.
+For other data sources, implement a [custom live signal](signals.md#live-signals).
+
+The [server-monitoring example](https://github.com/dos-group/vessim/blob/main/examples/live_server_monitoring.py) includes a Prometheus setup and optional Grafana dashboards.
+Use `Api(export_prometheus=True)` to expose simulation measurements for monitoring.
+
+## Build a digital twin
+
+Live measurements can keep a digital twin aligned with an operating energy system.
+Your application can update demand, battery charge, and other model parameters as conditions change.
+
+To explore possible futures, create separate [simulation studies](studies.md) initialized from the observed state.
+For example, start a study from the current battery charge and compare how long it could support demand during a grid outage.
+Your application supplies the observed state and forecast inputs to those simulations.
+
+See [controllers](controllers.md) for model updates and [results and metrics](results.md) for recording live experiments.
diff --git a/docs/concepts/optimization.md b/docs/concepts/optimization.md
new file mode 100644
index 00000000..38c63bbe
--- /dev/null
+++ b/docs/concepts/optimization.md
@@ -0,0 +1,79 @@
+# Optimization
+
+Simulation studies can span more configurations than you can reasonably test.
+Often, you want to find designs that are cost-effective, low-carbon, or resilient, and understand the trade-offs between those goals.
+
+Vessim lets you evaluate candidate designs with your own demand profiles, energy infrastructure, and control strategies.
+Connect those simulations to an optimizer such as [Optuna](https://optuna.org/) to search for promising designs using the [metrics](results.md) that matter to your study.
+
+## Size a battery
+
+Suppose your datacenter has solar generation and you are choosing a battery.
+More storage can reduce grid imports by saving surplus solar power for later, but it also costs more.
+Rather than choosing a single weighting between these goals, we can search for designs that offer different trade-offs.
+
+This example uses a synthetic solar profile and searches battery capacities between 0.5 and 5 kWh.
+It minimizes both battery purchase cost and grid imports over the six-hour scenario.
+Install Optuna with `pip install optuna`.
+
+```python
+import optuna
+import pandas as pd
+import vessim as vs
+
+solar = vs.Trace(pd.Series(
+ [0, 1200, 2400, 1200, 0, 0, 0],
+ index=[hour * 3600 for hour in range(7)],
+))
+
+
+def objective(trial):
+ capacity = trial.suggest_float("battery_capacity_wh", 500, 5000)
+ with vs.Environment("2026-01-01", step_size=1800) as env:
+ env.add_microgrid(
+ name="site",
+ actors=[vs.Consumer("load", 700), vs.Producer("solar", solar)],
+ dispatchables=[vs.SimpleBattery("battery", capacity), vs.GridConnection()],
+ )
+ recorder = env.add_controller(vs.Recorder(timeseries=False))
+ env.run(6 * 3600)
+
+ battery_cost = capacity / 1000 * 300 # Illustrative purchase cost in EUR/kWh
+ grid_import = recorder.metrics["microgrids"]["site"]["grid_import"]
+ return battery_cost, grid_import
+
+
+study = optuna.create_study(
+ directions=["minimize", "minimize"],
+ sampler=optuna.samplers.NSGAIISampler(seed=42),
+)
+study.optimize(objective, n_trials=20)
+
+for trial in study.best_trials:
+ print(trial.params, trial.values)
+```
+
+Optuna proposes a capacity, Vessim simulates it, and the objective function returns its cost and grid imports.
+`study.best_trials` contains the Pareto front among the evaluated designs.
+None of these designs improves one objective without worsening the other.
+You can choose a candidate based on your budget or import target.
+
+## Explore your design choices
+
+The same approach can search renewable capacity, generator sizing, or dispatch policy parameters.
+Use cost and emissions metrics as objectives, or assess resilience by simulating outages.
+To account for uncertain demand or weather, evaluate each candidate across several scenarios and compare its average or worst-case performance.
+
+The [complete infrastructure example](https://github.com/dos-group/vessim/blob/main/examples/infrastructure_study.py) searches wind, solar, and battery sizes over a full year and records each candidate's results.
+Its `--financial` mode compares annual cost with grid-import emissions using illustrative inputs.
+After following the [example setup instructions](https://github.com/dos-group/vessim/blob/main/examples/README.md), run:
+
+```console
+python examples/infrastructure_study.py --financial --trials 100 --output results/infrastructure-search
+python -m vessim view results/infrastructure-search
+```
+
+Explore the [annual optimization results](../viewer/index.html?study=optuna-search#results) from 100 equipment configurations in the viewer.
+
+Use [Optuna Dashboard](https://optuna-dashboard.readthedocs.io/en/stable/) to explore the search and its trade-offs, then inspect saved runs in the [Vessim viewer](../vessim_viewer.md) to understand why a design performs well.
+See Optuna's [multi-objective tutorial](https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/002_multi_objective.html) for more on configuring the search.
diff --git a/docs/concepts/results.md b/docs/concepts/results.md
new file mode 100644
index 00000000..71d78f42
--- /dev/null
+++ b/docs/concepts/results.md
@@ -0,0 +1,107 @@
+# Results and metrics
+
+How much electricity did your datacenter import, and when did the battery help?
+Record a simulation to inspect its time series and calculate metrics you can compare across runs.
+
+## Recorder
+
+Add a [`vs.Recorder`](../api_reference/recorder.md#vessim.Recorder) to your environment before running it:
+
+```python
+recorder = env.add_controller(vs.Recorder("results/experiment"))
+env.run(3600)
+```
+
+This saves the run's configuration and metrics in `experiment.json`, alongside its time series in `timeseries.csv`.
+Give each run its own output directory.
+Omit the directory to keep results in memory instead.
+
+After the run, use pandas to explore how your system behaved:
+
+```python
+frame = recorder.to_df()
+site = frame.xs("site", level="microgrid")
+print(site.head())
+```
+
+The columns include actor power, battery state of charge, and grid exchange.
+You can plot these in a notebook or use the CSV in your own analysis tools.
+To explore saved runs in the viewer:
+
+```sh
+python -m vessim view results/experiment
+```
+
+Try the [example results](../viewer/index.html?study=getting-started#results) and [time-series view](../viewer/index.html?study=getting-started#timeseries), or see [Vessim Viewer](../vessim_viewer.md) to open and share your own recordings.
+
+## ExperimentResult
+
+[`vs.ExperimentResult`](../api_reference/recorder.md#vessim.ExperimentResult) keeps a run's configuration, parameters, and metrics together.
+Access it through `recorder.result`, or load a saved run to continue your analysis:
+
+```python
+result = vs.ExperimentResult.load("results/experiment")
+print(result.metrics["microgrids"]["site"]["grid_import"])
+```
+
+You can also attach information calculated outside the simulation, such as an equipment cost, and save it with the result:
+
+```python
+result.add_metric("capex", 12000, "EUR")
+result.save("results/experiment/experiment.json")
+```
+
+## Metrics
+
+The recorder calculates energy consumption, generation, grid imports and exports in Wh, plus peak grid import and export in W.
+Read them from `recorder.metrics` or a saved result as shown above.
+See the [metric definitions](../api_reference/recorder.md#standard-metrics) for the full list.
+
+For larger studies, `Recorder(timeseries=False)` calculates metrics without storing time series.
+[Simulation studies](studies.md) shows how to compare configurations across many runs.
+
+### Costs and emissions
+
+To evaluate electricity costs or emissions, supply the corresponding price and carbon-intensity signals and add metric calculators to your recorder:
+
+```python
+env.add_microgrid(
+ name="site",
+ actors=[vs.Consumer("load", signal=700)],
+ dispatchables=[vs.GridConnection()],
+ grid_signals={
+ "price": 0.30, # EUR/kWh
+ "carbon": 0.25, # kgCO2e/kWh
+ },
+)
+recorder = env.add_controller(vs.Recorder(custom_metrics={
+ "cost": vs.ElectricityImportCost("site", "price", currency="EUR"),
+ "emissions": vs.GridImportEmissions("site", "carbon"),
+}))
+```
+
+These calculations combine imported electricity with your signals, which can also be [traces](signals.md#trace-signals) that vary over time.
+After the run, read `cost` and `emissions` alongside the standard metrics for `site`.
+[`vs.ElectricityExportRevenue`](../api_reference/recorder.md#vessim.ElectricityExportRevenue) can account for electricity sales using a separate export-price signal.
+
+### Custom metrics
+
+Subclass [`vs.Metric`](../api_reference/recorder.md#vessim.Metric) to calculate something specific to your study.
+For example, count the hours when grid import exceeds 1 kW:
+
+```python
+class HighImportHours(vs.Metric):
+ def __init__(self):
+ super().__init__("h", microgrid="site")
+
+ def step(self, now, states, duration):
+ imported_power = -states["site"]["p_grid"]
+ if imported_power > 1000:
+ self.value += duration / 3600
+```
+
+Vessim calls `step()` for each recorded interval, with its duration in seconds.
+Register the calculation as `custom_metrics={"high_import_hours": HighImportHours()}` on your recorder.
+Metrics can describe the whole run, a microgrid, or an individual actor.
+
+The [custom-metrics example](https://github.com/dos-group/vessim/blob/main/examples/custom_metrics.py) combines electricity costs, emissions, and equipment costs into study objectives.
diff --git a/docs/concepts/signals.md b/docs/concepts/signals.md
index d7df6196..4ee5bb48 100644
--- a/docs/concepts/signals.md
+++ b/docs/concepts/signals.md
@@ -1,101 +1,92 @@
-# Signals and Datasets
+# Actors and signals
-A [`Signal`](../api_reference/signal.md) provides a time-varying value to the simulation: the power consumed by a server, the power produced by a solar panel, the carbon intensity of the public grid. Every [`Actor`](../api_reference/actor.md) and every entry in a microgrid's `grid_signals` is backed by a `Signal`.
+[`vs.Consumer`](../api_reference/actor.md#vessim.Consumer) represents electrical loads, such as computing equipment or cooling.
+[`vs.Producer`](../api_reference/actor.md#vessim.Producer) represents generation, such as solar panels or wind turbines.
+Both are subclasses of [`vs.Actor`](../api_reference/actor.md#vessim.Actor), which also supports prosumers that consume and produce power.
-Vessim ships three signal types: [`StaticSignal`](../api_reference/signal.md#vessim.StaticSignal) for constant values, [`Trace`](../api_reference/signal.md#vessim.Trace) for replaying historical data, and [`SilSignal`](../api_reference/signal.md#vessim.SilSignal) for live data sources.
-You can easily write your own, only take care that `signal.at(elapsed)` returns the value at the elapsed time the simulation has accumulated since `sim_start`.
+Add these components to your microgrid's `actors` list.
+Their inputs can be static, follow a recorded trace, or come from live measurements.
-## StaticSignal
+## Static signals
-[`StaticSignal`](../api_reference/signal.md#vessim.StaticSignal) always returns the same value. It is useful for baseline loads, fixed production, or as a placeholder during testing:
+Actors with static inputs are the simplest form of actor.
+Pass a number to model fixed demand or generation:
```python
import vessim as vs
-signal = vs.StaticSignal(value=42)
+load = vs.Consumer("load", signal=700)
+solar = vs.Producer("solar", signal=1000)
```
-You can update a `StaticSignal` at runtime via `set_value()`. This allows a [`Controller`](../api_reference/controller.md) to toggle loads or follow a schedule.
+A [controller](controllers.md#demand-response) can change demand with `load.signal = 500`, which takes effect at the next actor update.
-## Trace
+## Trace signals
-[`Trace`](../api_reference/signal.md#vessim.Trace) replays a time series row-by-row. Its index is an *offset* in seconds since the trace's start (row 0 at `0`); as the simulation advances, `Trace` returns the value at the matching offset.
-
-### From in-memory data
-
-`Trace` accepts a pandas `Series` or `DataFrame` with either a numeric index (interpreted as seconds) or a `TimedeltaIndex`:
+Use [`vs.Trace`](../api_reference/signal.md#vessim.Trace) to describe demand or generation over time.
+Create a trace from a pandas Series or DataFrame:
```python
import pandas as pd
-import vessim as vs
-
-trace = vs.Trace(pd.Series([50, 200, 50], index=[0, 3600, 7200]))
-trace = vs.Trace(pd.Series(
- [50, 200, 50],
- index=pd.to_timedelta(["0s", "1h", "2h"]),
-))
+profile = pd.Series([0, 1000, 0], index=[0, 3600, 7200])
+solar = vs.Producer("solar", signal=vs.Trace(profile))
```
-Sampling does not need to be uniform. The index must start at 0.
+
Could not open results
+{visibleError}
+ + ) : catalog?.studies.length === 0 ? ( + <> +No studies available
++ Open an experiment, a study.json file, or a directory of + recorded experiments with vessim view. +
+ + ) : !context ? ( +Loading recorded summaries…
+ ) : ( +
+ {page === 'timeseries' ? (
+
+