Bump version to 1.2.0 (#61)

* chore: update version

* docs: small docs improvements

* docs: changelog update

* docs: changelog for 1.2.0

* fix: update custom objective doc name in tests

* docs: changelog fun

Author: ADGEfficiency

Makefile

@@ -51,7 +51,7 @@ test: setup-test test-docs
 create-test-docs: setup-test clean-test-docs
 	mkdir -p ./tests/phmdoctest
 	python -m phmdoctest README.md --outfile tests/phmdoctest/test_readme.py
-	python -m phmdoctest ./docs/docs/how-to/custom-objective.md  --outfile tests/phmdoctest/test_custom_objective.py
+	python -m phmdoctest ./docs/docs/how-to/custom-objectives.md  --outfile tests/phmdoctest/test_custom_objectives.py
 	python -m phmdoctest ./docs/docs/validation/battery.md --outfile tests/phmdoctest/test_validate_battery.py
 	python -m phmdoctest ./docs/docs/validation/evs.md --outfile tests/phmdoctest/test_validate_evs.py
 	python -m phmdoctest ./docs/docs/validation/heat-pump.md --outfile tests/phmdoctest/test_validate_heat-pump.py

docs/docs/changelog.md

@@ -1,6 +1,101 @@
 # Changelog
 
-## [1.1.1](https://github.com/ADGEfficiency/energy-py-linear/releases/tag/v1.1.0)
+## [1.2.0](https://github.com/ADGEfficiency/energy-py-linear/releases/tag/v1.2.0)
+
+### Custom Objective Functions
+
+A custom objective function allows users to create their own objective functions in the linear program.  
+
+This allows users to optimize for a custom set of revenues and costs. The objective function can target assets by type or name, and can include multiplication by interval data and/or a coefficient.
+
+The example below shows how to include a cost for battery use (a cycle cost) applied to the battery discharge:
+
+```python
+import numpy as np
+import energypylinear as epl
+
+assets = [
+    epl.Battery(power_mw=20, capacity_mwh=20)
+]
+site = epl.Site(
+    assets=assets,
+    electricity_prices=np.random.normal(0, 1000, 48)
+)
+terms=[
+    {
+        "asset_type":"site",
+        "variable":"import_power_mwh",
+        "interval_data":"electricity_prices"
+    },
+    {
+        "asset_type":"site",
+        "variable":"export_power_mwh",
+        "interval_data":"electricity_prices",
+        "coefficient":-1
+    },
+    {
+        "asset_type": "battery",
+        "variable": "electric_discharge_mwh",
+        "interval_data": "electricity_prices",
+        "coefficient": 0.25
+    }
+]
+site.optimize(objective={"terms": terms})
+```
+
+See [Custom Objectives](https://energypylinear.adgefficiency.com/latest/how-to/custom-objectives/) in the documentation for more examples.
+
+### Logging Improvements
+
+The dependency on `structlog` has been removed - we now only use `rich.logging.Console` to log to STDOUT. The ability to log to a file has been removed.
+
+The `verbose` flag now accepts either a `bool` or an `int`. The mapping of `verbose` to log levels is as follows:
+
+| `verbose` | Log Level |
+|-----------|-----------|
+| True      | INFO      |
+| False     | ERROR     |
+| 1         | DEBUG     |
+| 2         | INFO      |
+| 3         | WARNING   |
+| 4         | ERROR     |
+
+```python
+import energypylinear as epl
+asset = epl.Battery(electricity_prices=[10, -50, 200, -50, 200])
+simulation = asset.optimize(verbose=2)
+```
+
+```
+INFO     assets.site.optimize: cfg=<SiteConfig name=site, freq_mins=60,         
+         import_limit_mw=10000.0, export_limit_mw=10000.0>                      
+INFO     assets.site.optimize: cfg=<SiteConfig name=site, freq_mins=60,         
+         import_limit_mw=10000.0, export_limit_mw=10000.0>                      
+INFO     assets.site.optimize: assets=['battery', 'spill']                      
+INFO     assets.site.optimize: assets=['battery', 'spill']                      
+INFO     optimizer.solve: status='Optimal'                                      
+INFO     optimizer.solve: status='Optimal'                                      
+```
+
+### Tighten Optimizer Tolerance
+
+The default relative tolerance of the CBC optimizer has been reduced to `0.0`.
+
+### Optimizer Config can be a Dictionary
+
+It's now possible to use a dictionary in place of the `epl.OptimizerConfig` object:
+
+```python
+asset.optimize(optimizer_config={"timeout": 2, "relative_tolerance": 0.1})
+```
+
+### Other Changes
+
+We have upgraded Poetry to 1.7.0 and Mypy to 1.7.0.
+
+Plausible analytics added to the documentation.
+
+## [1.1.1](https://github.com/ADGEfficiency/energy-py-linear/releases/tag/v1.1.1)
 
 ### Bugs
 

docs/docs/how-to/custom-objective.md docs/docs/how-to/custom-objectives.md

@@ -1,10 +1,8 @@
-## Custom Objective Functions
+`energypylinear` has two different objective functions (price or carbon) built into the library.
 
-`energypylinear` can optimize for two different objective functions (price or carbon) built into the library. 
+However you may want to optimize for a different objective function in the linear program. You may have a business problem with a different set of revenues and costs than are included by default.
 
-However you may want to optimize for a different objective function in the linear program.
-
-**A custom objective function allows you to construct an objective function as you see fit** - allowing you to optimize a site and assets for the incentives and costs that are important to you.
+**A custom objective function allows you to construct an objective function as you see fit** - allowing you to optimize a site and assets for the revenues and costs that are important to you.
 
 Core to the custom objective function is the `epl.Term` - representing a single term in the objective function:
 
@@ -21,9 +19,9 @@ class Term:
     coefficient: float = 1.0
 ```
 
-Each term can target either many assets by type or one asset by name. It can also include multiplication by interval data or by a coefficient.
+A term can target either many assets by type or one asset by name. It can also include multiplication by interval data or by a coefficient.
 
-A custom objective function is a list of `epl.Term` - the sum of these terms becomes the objective function.
+A custom objective function is a list of terms:
 
 <!--phmdoctest-share-names-->
 ```python
@@ -32,11 +30,13 @@ class CustomObjectiveFunction:
     terms: list[Term]
 ```
 
+The objective function used in the linear program is the sum of these terms.
+
 ### Price and Carbon
 
-In this example we will show how to optimize a battery for an objective optimizes for both profit and carbon at the same time.
+This example shows how to optimize a battery for an objective that includes terms for both price and carbon.
 
-The example below creates an objective function where we incentive a site to:
+Below we create an objective function where we incentive a site to:
 
 - reduce import when the electricity price or carbon intensity is high,
 - increase export when the electricity price or carbon intensity is low.
@@ -52,9 +52,9 @@ def simulate(
     carbon_price: int,
     seed: int,
     n: int,
-    verbose: int = 2
+    verbose: int = 3
 ) -> epl.SimulationResult:
-    """Runs one battery simulation at a given carbon price with a custom objective function."""
+    """Run a battery simulation with a custom objective function."""
     np.random.seed(seed)
     site = epl.Site(
         assets=[epl.Battery(power_mw=10, capacity_mwh=20)],
@@ -100,14 +100,10 @@ print(simulate(carbon_price=50, seed=42, n=72))
 ```
 
 ```
-INFO     assets.site.optimize: cfg=<SiteConfig name=site, freq_mins=60,         
-         import_limit_mw=10000.0, export_limit_mw=10000.0>                      
-INFO     assets.site.optimize: assets=['battery']                               
-INFO     optimizer.solve: status='Optimal'                                      
 <energypylinear.SimulationResult feasible:True, rows:72, cols:28>
 ```
 
-We can validate that our custom objective function is working as expected by running simulations across many carbon prices, and see the effect on the profit and emissions of our site:
+We can validate that our custom objective function is working as expected by running simulations across many carbon prices:
 
 <!--phmdoctest-share-names-->
 ```python
@@ -148,7 +144,7 @@ In the previous example we used a custom objective function to apply incentives
 
 An example of this is a renewable energy certificate scheme, where the generation from one asset receives additional income for each MWh generated.
 
-In the example below, our `solar` asset receives additional income for each MWh generated.  
+In the example below, our `solar` asset receives additional income for each MWh generated.
 
 The site has a constrained export limit, which limits how much both generators can output. The site electric load increases in each interval, which allows us to see which generator is called first:
 
@@ -213,15 +209,15 @@ print(
 4                           50.0                          50.0
 ```
 
-As expected, the first generator that is called is the `solar` generator, as it receives additional income for it's output.  
+As expected, the first generator that is called is the `solar` generator, as it receives additional income for it's output.
 
 As the site demand increases, the `wind` generator is called to make up the remaining demand.
 
 ### Synthetic PPA
 
 A synthetic PPA is a financial instrument that allows swapping of the output of a wholesale exposed generator to a fixed price.
 
-This can be modelled as a custom objective function.  
+This can be modelled as a custom objective function.
 
 In the example below, we model a site with wholesale exposed import and export, and swap the output of our `wind` generator from the wholesale to a fixed price:
 
@@ -356,7 +352,7 @@ terms=[
 ]
 ```
 
-We can validate that this works by applying a stronger cycle cost and seeing the battery use descrease:
+We can validate that this works by applying a stronger cycle cost and seeing the battery use decrease:
 
 <!--phmdoctest-share-names-->
 ```python

docs/mkdocs.yml

@@ -63,7 +63,7 @@ nav:
       - Optimize Multiple Assets: how-to/dispatch-site.md
       - Optimize for Carbon: how-to/price-carbon.md
       - Optimize for a Forecast: how-to/dispatch-forecast.md
-      - Use a Custom Objective Function: how-to/custom-objective.md
+      - Custom Objective Functions: how-to/custom-objectives.md
   - Validation:
       - Battery: validation/battery.md
       # - Combined Heat & Power: validation/chp.md

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "energypylinear"
-version = "1.1.1"
+version = "1.2.0"
 description = "Optimizing energy assets with mixed-integer linear programming."
 authors = ["Adam Green <[email protected]>"]
 license = "MIT"