notebook10 redipatch and market clearing fix

[Manish-Khanra]

User description

Related Issue

Closes #838

Description

Checklist

• Documentation updated (docstrings, READMEs, user guides, inline comments, docs folder updates, etc.)
• New unit/integration tests added (if applicable)
• Changes noted in release notes (if any)
• Consent to release this PR's code under the GNU Affero General Public License v3.0

Additional Notes (optional)

• Reconfigured the Use Case 3 grid
• Added a summary table below the redispatch plot listing upward/downward volume per energy source, with a TOTAL row showing up = down.
• Updated the accompanying markdown explanations.
• Added a release note entry under Improvements.

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PR Type

Enhancement, Bug fix, Documentation

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Description

• Reworks Tutorial 10 redispatch scenario

• Adds gas peaker clearing behavior

• Balances redispatch with summary table

• Updates explanations and release notes

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Diagram Walkthrough

flowchart LR
  inputs["Tutorial 10 inputs"]
  clearing["Market clearing cases"]
  redispatch["Redispatch grid setup"]
  summary["Redispatch balance summary"]
  docs["Release notes"]
  inputs -- "adds gas peaker" --> clearing
  inputs -- "updates demand profiles" --> clearing
  inputs -- "repositions units and loads" --> redispatch
  redispatch -- "computes balanced volumes" --> summary
  summary -- "documented in" --> docs

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File Walkthrough

	Relevant files
Documentation	release_notes.rst Document Tutorial 10 redispatch improvements                          docs/source/release_notes.rst Adds an improvement note for Tutorial 10. Documents the redispatch use case rework. Notes balanced upward/downward redispatch volumes. Mentions the new redispatch summary table. +1/-0
Enhancement	10_DSU_and_flexibility.ipynb Rework clearing and redispatch tutorial scenario                  examples/notebooks/10_DSU_and_flexibility.ipynb Adds Gas CCGT with natural_gas fuel pricing to create a clearer merit-order and peaker-driven clearing price behavior. Revises demand profiles, market-clearing explanations, and pandas fill logic for Use Cases 1 and 2. Reconfigures Use Case 3 with northern renewable surplus, southern load and dispatchable plants, and removes the DSM unit for energy-consistent redispatch. Updates network plot coloring and adds a redispatch volume summary table with balanced upward/downward totals. +183/-60

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[github-actions]

PR Reviewer Guide 🔍

(Review updated until commit dcb7024)

Here are some key observations to aid the review process:

🎫 Ticket compliance analysis 🔶 838 - Partially compliant Compliant requirements: Rework Tutorial 10 redispatch scenario. Add gas peaker / Gas CCGT market-clearing behavior with natural gas pricing. Update demand profiles and clearing explanations. Reconfigure Use Case 3 grid for the redispatch demo. Add a redispatch summary table. Update accompanying notebook markdown explanations. Add a release note entry under Improvements. Non-compliant requirements: Balance redispatch volumes so total upward volume equals total downward volume in the summary table. Add a redispatch summary table with a TOTAL row that clearly shows upward volume equals downward volume. Requires further human verification: Verify the notebook runs end-to-end after the scenario changes. Verify the rendered plots and tables visually demonstrate the intended redispatch behavior.

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Summary Bug The redispatch table sums downward redispatch with clip(upper=0), so downward volumes remain negative. The TOTAL row will therefore show values like upward 100 and downward -100, which does not satisfy the stated requirement that the table show upward volume equals downward volume. Store downward volume as a positive magnitude, and keep the signed net in the separate net column. " \"Upward / ramp-up (MWh)\": redisp_p_set.clip(lower=0).sum(),\n", " \"Downward / curtailment (MWh)\": redisp_p_set.clip(upper=0).sum(),\n", " }\n", ").dropna(subset=[\"Node\"])\n", "redispatch_summary[\"Net (MWh)\"] = (\n", " redispatch_summary[\"Upward / ramp-up (MWh)\"]\n", " + redispatch_summary[\"Downward / curtailment (MWh)\"]\n", ")\n", "redispatch_summary = redispatch_summary.round(1).sort_values(\"Net (MWh)\")\n", "\n", "# Totals row: total upward volume == total downward volume -> balanced, no backup\n", "total_row = pd.DataFrame(\n", " {\n", " \"Node\": \"\",\n", " \"Technology\": \"ALL UNITS\",\n", " \"Upward / ramp-up (MWh)\": round(redisp_p_set.clip(lower=0).values.sum(), 1),\n", " \"Downward / curtailment (MWh)\": round(\n", " redisp_p_set.clip(upper=0).values.sum(), 1\n", " ),\n", " \"Net (MWh)\": round(redisp_p_set.values.sum(), 1) + 0.0,\n",

[github-actions]

PR Code Suggestions ✨

Latest suggestions up to dcb7024

Explore these optional code suggestions:

Category	Suggestion	Impact
General	Correct energy volume calculation redisp_p_set appears to contain power values per snapshot, so summing it directly labels MW-snapshot totals as MWh and overstates volumes for 15-minute data. Convert power to energy using the snapshot duration, and report downward curtailment as a positive volume so the balance check is numerically clear. examples/notebooks/10_DSU_and_flexibility.ipynb [2959-2985] +timestep_hours = ( + redisp_p_set.index.to_series().diff().dt.total_seconds().div(3600).median() +) +redisp_energy = redisp_p_set * timestep_hours + redispatch_summary = pd.DataFrame( { "Node": pp_info["node"], "Technology": pp_info["technology"], - "Upward / ramp-up (MWh)": redisp_p_set.clip(lower=0).sum(), - "Downward / curtailment (MWh)": redisp_p_set.clip(upper=0).sum(), + "Upward / ramp-up (MWh)": redisp_energy.clip(lower=0).sum(), + "Downward / curtailment (MWh)": -redisp_energy.clip(upper=0).sum(), } ).dropna(subset=["Node"]) redispatch_summary["Net (MWh)"] = ( redispatch_summary["Upward / ramp-up (MWh)"] - + redispatch_summary["Downward / curtailment (MWh)"] + - redispatch_summary["Downward / curtailment (MWh)"] ) redispatch_summary = redispatch_summary.round(1).sort_values("Net (MWh)") # Totals row: total upward volume == total downward volume -> balanced, no backup total_row = pd.DataFrame( { "Node": "", "Technology": "ALL UNITS", - "Upward / ramp-up (MWh)": round(redisp_p_set.clip(lower=0).values.sum(), 1), + "Upward / ramp-up (MWh)": round(redisp_energy.clip(lower=0).values.sum(), 1), "Downward / curtailment (MWh)": round( - redisp_p_set.clip(upper=0).values.sum(), 1 + -redisp_energy.clip(upper=0).values.sum(), 1 ), - "Net (MWh)": round(redisp_p_set.values.sum(), 1) + 0.0, + "Net (MWh)": round(redisp_energy.values.sum(), 1) + 0.0, }, index=["TOTAL"], ) Suggestion importance[1-10]: 7 __ Why: The suggestion is relevant because redisp_p_set is likely a power time series, so directly summing it can mislabel MW-snapshot totals as MWh, especially with 15-minute snapshots. Reporting downward curtailment as a positive value also makes the balance check in redispatch_summary clearer, though this mainly affects tutorial/result presentation rather than core simulation logic.	Medium

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Previous suggestions

Suggestions up to commit 2c0928c

Category	Suggestion	Impact
Possible issue	Correct energy volume calculation redisp_p_set appears to contain power values, so summing it directly over 15-minute snapshots reports MW-timesteps rather than MWh. Convert the redispatch time series to energy using the snapshot duration, and store downward curtailment as a positive volume so the balance check is numerically meaningful. examples/notebooks/10_DSU_and_flexibility.ipynb [2959-2985] +snapshot_hours = redisp_p_set.index.to_series().diff().dt.total_seconds().div(3600) +snapshot_hours.iloc[0] = snapshot_hours.iloc[1] if len(snapshot_hours) > 1 else 1 +redisp_energy = redisp_p_set.mul(snapshot_hours, axis=0) + +upward_mwh = redisp_energy.clip(lower=0).sum() +downward_mwh = -redisp_energy.clip(upper=0).sum() + redispatch_summary = pd.DataFrame( { "Node": pp_info["node"], "Technology": pp_info["technology"], - "Upward / ramp-up (MWh)": redisp_p_set.clip(lower=0).sum(), - "Downward / curtailment (MWh)": redisp_p_set.clip(upper=0).sum(), + "Upward / ramp-up (MWh)": upward_mwh, + "Downward / curtailment (MWh)": downward_mwh, } ).dropna(subset=["Node"]) redispatch_summary["Net (MWh)"] = ( redispatch_summary["Upward / ramp-up (MWh)"] - + redispatch_summary["Downward / curtailment (MWh)"] + - redispatch_summary["Downward / curtailment (MWh)"] ) redispatch_summary = redispatch_summary.round(1).sort_values("Net (MWh)") # Totals row: total upward volume == total downward volume -> balanced, no backup total_row = pd.DataFrame( { "Node": "", "Technology": "ALL UNITS", - "Upward / ramp-up (MWh)": round(redisp_p_set.clip(lower=0).values.sum(), 1), - "Downward / curtailment (MWh)": round( - redisp_p_set.clip(upper=0).values.sum(), 1 - ), - "Net (MWh)": round(redisp_p_set.values.sum(), 1) + 0.0, + "Upward / ramp-up (MWh)": round(upward_mwh.sum(), 1), + "Downward / curtailment (MWh)": round(downward_mwh.sum(), 1), + "Net (MWh)": round(redisp_energy.values.sum(), 1) + 0.0, }, index=["TOTAL"], ) Suggestion importance[1-10]: 7 __ Why: The suggestion correctly identifies that summing redisp_p_set directly can mislabel MW-snapshot sums as MWh, especially with 15-minute snapshots. Converting by snapshot duration and reporting downward curtailment as a positive volume improves the correctness and readability of the redispatch summary.	Medium
	Preserve expected scenario file Deleting industrial_dsm_units.csv can break downstream scenario loading if the loader expects the unit file to exist. Keep the file present but empty, preserving its columns, so Use Case 3 has no DSM units without introducing a missing-file failure. examples/notebooks/10_DSU_and_flexibility.ipynb [2741-2746] dsm_path = f"{scenario_path}/industrial_dsm_units.csv" if os.path.exists(dsm_path): - os.remove(dsm_path) + industrial_dsm_units = pd.read_csv(dsm_path) + industrial_dsm_units.iloc[0:0].to_csv(dsm_path, index=False) print("Steel plant (DSM unit) removed from the Use Case 3 scenario.") else: print("No DSM unit file present - nothing to remove.") Suggestion importance[1-10]: 5 __ Why: Keeping an empty industrial_dsm_units.csv is a reasonable robustness improvement if downstream loading expects the file to exist. However, the PR may intentionally rely on missing optional files to exclude DSM units, so the impact is somewhat speculative.	Low
General	Validate scenario remapping map silently writes NaN for any plant name that is not listed in node_by_name or max_power_by_name, which can corrupt the scenario inputs. Validate the mapping before overwriting these required columns so the notebook fails clearly if the input CSV changes. examples/notebooks/10_DSU_and_flexibility.ipynb [2689-2690] +missing_nodes = set(powerplant_units["name"]) - set(node_by_name) +missing_max_power = set(powerplant_units["name"]) - set(max_power_by_name) +if missing_nodes or missing_max_power: + raise ValueError( + "Missing Use Case 3 redispatch configuration for plants: " + f"{sorted(missing_nodes | missing_max_power)}" + ) + powerplant_units["node"] = powerplant_units["name"].map(node_by_name) powerplant_units["max_power"] = powerplant_units["name"].map(max_power_by_name) Suggestion importance[1-10]: 5 __ Why: The suggestion is valid because map would silently introduce NaN values if powerplant_units["name"] changes. Adding validation improves failure clarity, but it is a maintainability safeguard rather than a direct bug fix in the current fixed tutorial data.	Low

Suggestions up to commit c74dacc

Category	Suggestion	Impact
Possible issue	Correct energy aggregation redisp_p_set is power per snapshot, so summing it directly over 15-minute snapshots reports quarter-hour MW values as MWh and overstates volumes. Convert each snapshot to energy using the timestep duration, and store downward curtailment as a positive volume so the table's balance check is unambiguous. examples/notebooks/10_DSU_and_flexibility.ipynb [2959-2985] +snapshot_hours = redisp_p_set.index.to_series().diff().dt.total_seconds().div(3600) +snapshot_hours.iloc[0] = snapshot_hours.iloc[1] if len(snapshot_hours) > 1 else 1 + +redisp_energy = redisp_p_set.mul(snapshot_hours, axis=0) +upward = redisp_energy.clip(lower=0).sum() +downward = -redisp_energy.clip(upper=0).sum() + redispatch_summary = pd.DataFrame( { "Node": pp_info["node"], "Technology": pp_info["technology"], - "Upward / ramp-up (MWh)": redisp_p_set.clip(lower=0).sum(), - "Downward / curtailment (MWh)": redisp_p_set.clip(upper=0).sum(), + "Upward / ramp-up (MWh)": upward, + "Downward / curtailment (MWh)": downward, } ).dropna(subset=["Node"]) redispatch_summary["Net (MWh)"] = ( redispatch_summary["Upward / ramp-up (MWh)"] - + redispatch_summary["Downward / curtailment (MWh)"] + - redispatch_summary["Downward / curtailment (MWh)"] ) redispatch_summary = redispatch_summary.round(1).sort_values("Net (MWh)") # Totals row: total upward volume == total downward volume -> balanced, no backup total_row = pd.DataFrame( { "Node": "", "Technology": "ALL UNITS", - "Upward / ramp-up (MWh)": round(redisp_p_set.clip(lower=0).values.sum(), 1), - "Downward / curtailment (MWh)": round( - redisp_p_set.clip(upper=0).values.sum(), 1 - ), - "Net (MWh)": round(redisp_p_set.values.sum(), 1) + 0.0, + "Upward / ramp-up (MWh)": round(upward.sum(), 1), + "Downward / curtailment (MWh)": round(downward.sum(), 1), + "Net (MWh)": round(redisp_energy.values.sum(), 1) + 0.0, }, index=["TOTAL"], ) Suggestion importance[1-10]: 7 __ Why: The suggestion correctly identifies that summing redisp_p_set power values can misreport energy volumes as MWh when snapshots are sub-hourly. Converting to energy and making downward curtailment positive improves the accuracy and readability of the redispatch summary table.	Medium
General	Validate scenario mappings map silently writes NaN for any plant name not present in node_by_name or max_power_by_name, which can corrupt the scenario CSV and make the later market run fail in a hard-to-trace way. Validate the mapping result before overwriting powerplant_units. examples/notebooks/10_DSU_and_flexibility.ipynb [2689-2690] -powerplant_units["node"] = powerplant_units["name"].map(node_by_name) -powerplant_units["max_power"] = powerplant_units["name"].map(max_power_by_name) +mapped_nodes = powerplant_units["name"].map(node_by_name) +mapped_max_power = powerplant_units["name"].map(max_power_by_name) +missing_mapping = powerplant_units.loc[ + mapped_nodes.isna() | mapped_max_power.isna(), "name" +].tolist() +if missing_mapping: + raise ValueError( + f"Missing Use Case 3 node/max_power mapping for: {missing_mapping}" + ) + +powerplant_units["node"] = mapped_nodes +powerplant_units["max_power"] = mapped_max_power + Suggestion importance[1-10]: 5 __ Why: The suggestion is valid because map would silently introduce NaN values for unmapped plant names, which could make the scenario fail later. This is a useful robustness improvement, but the mapping appears intentionally exhaustive for the tutorial data, so the impact is moderate.	Low

Suggestions up to commit c238685

Category	Suggestion	Impact
General	Convert power to energy redisp_p_set contains power values, so summing them directly does not produce MWh; with 15-minute snapshots this overstates volumes by a factor of four. Convert MW to energy using the snapshot duration, and make curtailment positive so the balance check is readable. examples/notebooks/10_DSU_and_flexibility.ipynb [2959-2985] +snapshot_hours = redisp_p_set.index.to_series().diff().dt.total_seconds().div(3600) +snapshot_hours.iloc[0] = ( + snapshot_hours.dropna().iloc[0] if snapshot_hours.notna().any() else 1.0 +) +redisp_energy = redisp_p_set.mul(snapshot_hours.to_numpy(), axis=0) + +upward = redisp_energy.clip(lower=0).sum() +downward = -redisp_energy.clip(upper=0).sum() + redispatch_summary = pd.DataFrame( { "Node": pp_info["node"], "Technology": pp_info["technology"], - "Upward / ramp-up (MWh)": redisp_p_set.clip(lower=0).sum(), - "Downward / curtailment (MWh)": redisp_p_set.clip(upper=0).sum(), + "Upward / ramp-up (MWh)": upward, + "Downward / curtailment (MWh)": downward, } ).dropna(subset=["Node"]) redispatch_summary["Net (MWh)"] = ( redispatch_summary["Upward / ramp-up (MWh)"] - + redispatch_summary["Downward / curtailment (MWh)"] + - redispatch_summary["Downward / curtailment (MWh)"] ) redispatch_summary = redispatch_summary.round(1).sort_values("Net (MWh)") # Totals row: total upward volume == total downward volume -> balanced, no backup total_row = pd.DataFrame( { "Node": "", "Technology": "ALL UNITS", - "Upward / ramp-up (MWh)": round(redisp_p_set.clip(lower=0).values.sum(), 1), - "Downward / curtailment (MWh)": round( - redisp_p_set.clip(upper=0).values.sum(), 1 - ), - "Net (MWh)": round(redisp_p_set.values.sum(), 1) + 0.0, + "Upward / ramp-up (MWh)": round(upward.sum(), 1), + "Downward / curtailment (MWh)": round(downward.sum(), 1), + "Net (MWh)": round(redisp_energy.values.sum(), 1) + 0.0, }, index=["TOTAL"], ) Suggestion importance[1-10]: 7 __ Why: The suggestion correctly identifies that summing redisp_p_set power values directly and labeling them MWh can produce incorrect redispatch volumes, especially for sub-hourly snapshots. Converting by snapshot duration and reporting downward curtailment as a positive volume would make the summary table substantially more accurate and readable.	Medium
Possible issue	Validate required mappings map() silently writes NaN for any plant name that is not present in node_by_name or max_power_by_name, which can make the scenario fail later with invalid node or max_power values. Validate that every powerplant_units["name"] is covered before overwriting these required columns. examples/notebooks/10_DSU_and_flexibility.ipynb [2689-2690] -powerplant_units["node"] = powerplant_units["name"].map(node_by_name) -powerplant_units["max_power"] = powerplant_units["name"].map(max_power_by_name) +mapped_nodes = powerplant_units["name"].map(node_by_name) +mapped_max_power = powerplant_units["name"].map(max_power_by_name) +missing_mappings = powerplant_units.loc[ + mapped_nodes.isna() | mapped_max_power.isna(), "name" +].tolist() +if missing_mappings: + raise ValueError( + f"Missing Use Case 3 node/max_power mapping for: {missing_mappings}" + ) + +powerplant_units["node"] = mapped_nodes +powerplant_units["max_power"] = mapped_max_power + Suggestion importance[1-10]: 5 __ Why: The suggestion is correct that map() can silently introduce NaN values for unmapped plant names, which would make node or max_power invalid later. The current notebook appears to define mappings for all known plants, so this is mainly a defensive robustness improvement rather than a clear bug fix.	Low
	Preserve empty input file Deleting industrial_dsm_units.csv can break loaders that expect the configured unit file to exist even when it contains no active units. Keep the file with its schema and zero rows instead, so Use Case 3 removes the DSM unit without creating a missing-file failure. examples/notebooks/10_DSU_and_flexibility.ipynb [2741-2746] dsm_path = f"{scenario_path}/industrial_dsm_units.csv" if os.path.exists(dsm_path): - os.remove(dsm_path) + industrial_dsm_units = pd.read_csv(dsm_path).iloc[0:0] + industrial_dsm_units.to_csv(dsm_path, index=False) print("Steel plant (DSM unit) removed from the Use Case 3 scenario.") else: print("No DSM unit file present - nothing to remove.") Suggestion importance[1-10]: 5 __ Why: Keeping industrial_dsm_units.csv with headers and zero rows is a plausible way to avoid missing-file issues while removing active DSM units. However, the PR intentionally deletes the file and the suggestion depends on loader behavior outside the shown diff, so the impact is uncertain.	Low

Suggestions up to commit c238685

Category	Suggestion	Impact
General	Correct energy volume totals The Downward / curtailment (MWh) column is currently negative, so the table does not show comparable upward and downward volumes despite the text claiming equality. Also convert redisp_p_set from MW to MWh using the snapshot duration before summing, otherwise 15-minute data will be overstated. examples/notebooks/10_DSU_and_flexibility.ipynb [2959-2985] +snapshot_hours = redisp_p_set.index.to_series().diff().dt.total_seconds().div(3600) +snapshot_hours.iloc[0] = snapshot_hours.iloc[1] if len(snapshot_hours) > 1 else 1.0 +redisp_energy = redisp_p_set.mul(snapshot_hours, axis=0) + +upward = redisp_energy.clip(lower=0).sum() +downward = -redisp_energy.clip(upper=0).sum() + redispatch_summary = pd.DataFrame( { "Node": pp_info["node"], "Technology": pp_info["technology"], - "Upward / ramp-up (MWh)": redisp_p_set.clip(lower=0).sum(), - "Downward / curtailment (MWh)": redisp_p_set.clip(upper=0).sum(), + "Upward / ramp-up (MWh)": upward, + "Downward / curtailment (MWh)": downward, } ).dropna(subset=["Node"]) redispatch_summary["Net (MWh)"] = ( redispatch_summary["Upward / ramp-up (MWh)"] - + redispatch_summary["Downward / curtailment (MWh)"] + - redispatch_summary["Downward / curtailment (MWh)"] ) redispatch_summary = redispatch_summary.round(1).sort_values("Net (MWh)") # Totals row: total upward volume == total downward volume -> balanced, no backup +total_upward = upward.sum() +total_downward = downward.sum() total_row = pd.DataFrame( { "Node": "", "Technology": "ALL UNITS", - "Upward / ramp-up (MWh)": round(redisp_p_set.clip(lower=0).values.sum(), 1), - "Downward / curtailment (MWh)": round( - redisp_p_set.clip(upper=0).values.sum(), 1 - ), - "Net (MWh)": round(redisp_p_set.values.sum(), 1) + 0.0, + "Upward / ramp-up (MWh)": round(total_upward, 1), + "Downward / curtailment (MWh)": round(total_downward, 1), + "Net (MWh)": round(total_upward - total_downward, 1), }, index=["TOTAL"], ) Suggestion importance[1-10]: 7 __ Why: This is a valid correctness improvement for the new redispatch_summary: the current downward values are negative despite being described as comparable volumes, and summing redisp_p_set as MWh is questionable if it is MW over sub-hourly snapshots. The impact is meaningful for notebook result accuracy, though it affects explanatory output rather than core functionality.	Medium
Possible issue	Validate scenario remapping Directly assigning map() results will write NaN into node and max_power for any plant name not present in the dictionaries. Validate the mapping first so the notebook fails early instead of producing an invalid scenario CSV. examples/notebooks/10_DSU_and_flexibility.ipynb [2689-2690] +missing_units = powerplant_units.loc[ + ~powerplant_units["name"].isin(node_by_name) + | ~powerplant_units["name"].isin(max_power_by_name), + "name", +] +if not missing_units.empty: + raise ValueError(f"Missing Use Case 3 redispatch mapping for units: {missing_units.tolist()}") + powerplant_units["node"] = powerplant_units["name"].map(node_by_name) powerplant_units["max_power"] = powerplant_units["name"].map(max_power_by_name) Suggestion importance[1-10]: 4 __ Why: The suggestion is correct that map() would silently produce NaN for unmapped powerplant_units["name"] values, so early validation would make the notebook more robust. However, the dictionaries in this PR appear to cover the intended units, so this is a defensive maintainability improvement rather than a demonstrated bug.	Low

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Persistent review updated to latest commit c238685

[github-actions]

Persistent review updated to latest commit c238685

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✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 83.35%. Comparing base (b0a4943) to head (dcb7024).

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[github-actions]

Persistent review updated to latest commit c74dacc

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Persistent review updated to latest commit 2c0928c

[github-actions]

Persistent review updated to latest commit dcb7024

[mthede]

Explanation below does not fit to the code ("The demand pattern fluctuates between 44,000 MW and 46,000 MW...")

[mthede]

Why do you want to have this dedicated redispatch example in this notebook if the steelplant is not part of it? What additional value do you see here, in comparison to the notebook 11 for showcasing redispatch?

[mthede]

Other comments not related to changed code:

• doesn't compile formula:
• l.1705: meaning unclear: "Transmission grid: Converts the reduced iron into steel."
• l.1989: might not render correctly?
• l.2059: simulation throws warnings/errors:

- l.2406: congestion plot may be broken as well? - l.2591: ~ causes strikethrough formatting

[mthede]

see my other comments