index update

Author: arght

chapters/01-introduction/01-energy-system-models.qmd

@@ -0,0 +1,3 @@
+## What is energy system modelling?
+
+<!-- TODO: Add section content. -->

chapters/01-introduction/02-optimisation.qmd

@@ -0,0 +1,3 @@
+## Mathematical optimisation
+
+<!-- TODO: Add section content. -->

chapters/01-introduction/03-computer-implementation.qmd

@@ -0,0 +1,15 @@
+## Computer implementation
+
+- Must be defined in blocks. For example, a set and all its subsets
+should constitute one block in the sets section.
+- Names are intended to be meaningful. Follow conventions
+  - Items with the same name represent the same concept in different
+models
+  - Units should be used in all definitions
+  - Parameters are named pParameterName (e.g., pOperReserveDw)
+  - Variables are named vVariableName (e.g., vReserveDown)
+  - Equations are named eEquationName (e.g., eOperReserveDw)
+  - Use short set names (one or two letters) for easier reading
+- Equations are laid out as clearly as possible
+- Use the same layout for all equations, with the left-hand side on one line and the right-hand side on another line, indented by a tab
+- Good Optimisation Modelling Practices

chapters/01-introduction/index.qmd

@@ -0,0 +1,7 @@
+# Introduction
+
+{{< include 01-energy-system-models.qmd >}}
+
+{{< include 02-optimisation.qmd >}}
+
+{{< include 03-computer-implementation.qmd >}}

chapters/03-economic-dispatch/01-description.qmd

@@ -0,0 +1,3 @@
+## Description
+
+<!-- TODO: Add section content. -->

chapters/03-economic-dispatch/02-formulation.qmd

@@ -0,0 +1,3 @@
+## Formulation
+
+<!-- TODO: Add section content. -->

chapters/03-economic-dispatch/03-example-in-python.qmd

@@ -0,0 +1,61 @@
+---
+jupyter: compass-python
+---
+
+## Example in Python
+
+This example solves the economic dispatch problem with Pyomo and HiGHS. The reusable implementation lives in `examples/python/dispatch_model.py`; this section imports that script, defines a small generator fleet, and reports the least-cost dispatch.
+
+```{python}
+from pathlib import Path
+import sys
+
+for project_root in [Path.cwd(), *Path.cwd().parents]:
+    examples_dir = project_root / "examples" / "python"
+    if (examples_dir / "dispatch_model.py").exists():
+        sys.path.insert(0, str(examples_dir))
+        break
+else:
+    raise FileNotFoundError("Could not find examples/python/dispatch_model.py")
+
+from dispatch_model import Generator, merit_order_dispatch, total_cost
+```
+
+The generator data include a zero-marginal-cost wind unit, a low-cost solar unit, and a gas unit that covers the remaining demand.
+
+```{python}
+generators = [
+    Generator("wind", marginal_cost=0.0, capacity=35.0),
+    Generator("solar", marginal_cost=3.0, capacity=25.0),
+    Generator("gas", marginal_cost=75.0, capacity=60.0),
+]
+
+demand = 80.0
+```
+
+The solver chooses generation from the cheapest available units first, while enforcing the demand balance and each unit's capacity limit.
+
+```{python}
+dispatch = merit_order_dispatch(demand, generators)
+cost = total_cost(dispatch, generators)
+
+dispatch, cost
+```
+
+The solution uses all available wind and solar output, then dispatches gas for the remaining demand.
+
+```{python}
+for generator_name, output in dispatch:
+    print(f"{generator_name}: {output:.1f} MW")
+
+print(f"Total cost: {cost:.2f}")
+```
+
+If demand exceeds total available capacity, the model is infeasible and the example raises a clear error.
+
+```{python}
+try:
+    merit_order_dispatch(200.0, generators)
+except ValueError as error:
+    print(error)
+```

chapters/03-economic-dispatch/04-example-in-julia.qmd

@@ -0,0 +1,49 @@
+## Example in Julia
+
+This example solves the economic dispatch problem with JuMP and HiGHS. The reusable implementation lives in `examples/julia/dispatch_model.jl`; this section includes that script, defines a small generator fleet, and reports the least-cost dispatch.
+
+```{julia}
+include(joinpath(@__DIR__, "..", "..", "examples", "julia", "dispatch_model.jl"))
+using .DispatchModel
+```
+
+The generator data include a zero-marginal-cost wind unit, a low-cost solar unit, and a gas unit that covers the remaining demand.
+
+```{julia}
+generators = [
+    Generator("wind", 0.0, 35.0),
+    Generator("solar", 3.0, 25.0),
+    Generator("gas", 75.0, 60.0),
+]
+
+demand = 80.0
+```
+
+The solver chooses generation from the cheapest available units first, while enforcing the demand balance and each unit's capacity limit.
+
+```{julia}
+dispatch = merit_order_dispatch(demand, generators)
+cost = total_cost(dispatch, generators)
+
+dispatch, cost
+```
+
+The solution uses all available wind and solar output, then dispatches gas for the remaining demand.
+
+```{julia}
+for (generator_name, output) in dispatch
+    println("$generator_name: $(round(output; digits = 1)) MW")
+end
+
+println("Total cost: $(round(cost; digits = 2))")
+```
+
+If demand exceeds total available capacity, the model is infeasible and the example raises a clear error.
+
+```{julia}
+try
+    merit_order_dispatch(200.0, generators)
+catch error
+    println(error)
+end
+```

chapters/03-economic-dispatch/index.qmd

@@ -0,0 +1,7 @@
+# Economic Dispatch
+
+{{< include 01-description.qmd >}}
+
+{{< include 02-formulation.qmd >}}
+
+{{< include 04-example-in-julia.qmd >}}

chapters/04-unit-commitment/01-description.qmd

@@ -0,0 +1,3 @@
+## Description
+
+<!-- TODO: Add section content. -->