Thermal model updates: feasibility & unit tests #374

werdnum · 2024-11-13T14:28:41Z

Three significant changes:

Add an option (to become the default?) to use penalties rather than constraints for thermal conditions. This will replace a bunch of hacks I'm using to avoid trying to ask for something impossible from the solver.
Rework overshoot temperature - instead of "never exceed this temperature", we want "if we do exceed this temperature, it shouldn't be our fault" - implemented as "load must be off if we overshoot"
Added relatively comprehensive unit tests to cover the above behaviours plus more.

Summary by Sourcery

Enhance thermal management in optimization model by introducing more flexible temperature constraint handling and comprehensive unit tests

New Features:

Add support for penalty-based thermal constraints instead of hard constraints
Implement more nuanced temperature overshoot handling for both heating and cooling scenarios

Enhancements:

Refactor thermal load optimization to support different modes of temperature management
Improve flexibility in handling thermal load constraints

Tests:

Add comprehensive unit tests for thermal management scenarios
Implement test cases covering heating and cooling temperature constraints
Create tests for both constraint and penalty-based thermal management modes

1. Add option to operate on a penalty basis instead of a constraint basis. This means that we will provide a suboptimal solution rather than fail out and produce nonsense. 2. Rework overshoot constraint such that instead of saying "the temperature shall not exceed this value", we are now saying "if the temperature goes past the overshoot point, the load must be off the previous timestep". In other words, we can overshoot, but if we do, it can't be because of us.

…milar are not specified for all deferred loads (treat them as the default)

davidusb-geek · 2024-12-20T13:24:03Z

Hi @werdnum, I finally gathered some small spare time to look at this. It seems like a very nice contribution, this may help with unexplainable infeasible results we get some time. I should probably think about moving to this type of reasoning and implementation: penalizing instead of constraining. On downside of this is that if you ask the "imposible" to the algorithm then it will converge but the result may possible have no real physical meaning.

What about this PR, it is in a Draft version? Do you think that you could package this into a meargeable PR?

sonarqubecloud · 2025-02-01T16:41:08Z

Quality Gate passed

Issues
49 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud

Micr0mega · 2025-02-10T15:33:13Z

Any updates on dropping the draft status? I'd love this, since I'm using the thermal model to control my heat pump, which is connected to a solar boiler. The temperatures will, in summer time, for sure rise beyond the overshoot temperature, without running the heat pump itself. This currently will mean my model will be infeasible very often.

GeoDerp · 2025-04-19T06:20:39Z

@sourcery-ai review

sourcery-ai · 2025-04-19T06:20:42Z

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davidusb-geek · 2025-04-19T07:31:39Z

@sourcery-ai review

sourcery-ai · 2025-04-19T07:32:52Z

Reviewer's Guide by Sourcery

This pull request introduces significant updates to the thermal model, including the option to use penalties instead of constraints, reworked overshoot temperature handling, and comprehensive unit tests. The test forecast setup was also refactored for better maintainability.

No diagrams generated as the changes look simple and do not need a visual representation.

File-Level Changes

Change	Details	Files
Added option to use penalties instead of constraints for thermal conditions.	Added a 'mode' option to thermal_config to switch between 'constrain' and 'penalize'. When penalizing, a penalty_factor can be specified to adjust the impact of temperature deviations. Introduced a penalty_var to represent the penalty value, ensuring it's non-positive. Modified the objective function to include the penalty_var, influencing the optimization towards desired temperatures.	`src/emhass/optimization.py`
Reworked overshoot temperature handling for thermal loads.	Introduced an is_overshoot binary variable to indicate if the temperature exceeds the overshoot_temperature. Added constraints to ensure is_overshoot is correctly set based on the predicted temperature. Added a constraint to ensure that the load must be off if an overshoot occurs, preventing further temperature increase/decrease. Added sense_coeff to handle both heating and cooling scenarios.	`src/emhass/optimization.py`
Added comprehensive unit tests for thermal load behaviors.	Created a run_thermal_forecast method to streamline thermal forecast testing. Added tests for thermal management with constraints, including overshoot scenarios for both heating and cooling. Added tests for thermal management with penalties, covering both heating and cooling. Added tests to verify that the overshoot temperature is not exceeded and that temperature requirements are met. Added tests to verify that the load is turned off when the overshoot temperature is exceeded.	`tests/test_optimization.py`
Refactored the test forecast setup.	Consolidated forecast setup into a single run_test_forecast method. Made the prediction_horizon configurable. Simplified the passing of data to the forecast. Updated the optimization configuration to use num_def_loads instead of number_of_deferrable_loads.	`tests/test_optimization.py`

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sourcery-ai

Hey @werdnum - I've reviewed your changes - here's some feedback:

Overall Comments:

The new tests look good, but it's a bit hard to understand what they're testing without more context.
Consider adding a docstring to the new run_test_forecast method to explain its purpose and parameters.

Here's what I looked at during the review

🟡 General issues: 2 issues found
🟢 Security: all looks good
🟢 Testing: all looks good
🟢 Complexity: all looks good
🟢 Documentation: all looks good

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sourcery-ai · 2025-04-19T07:32:53Z

src/emhass/optimization.py

@@ -402,7 +408,7 @@ def create_matrix(input_list, n):
                            rhs = 0)
                        for i in set_I})

-            elif "def_load_config" in self.optim_conf.keys():
+            elif "def_load_config" in self.optim_conf.keys() and len(self.optim_conf["def_load_config"]) > k:


suggestion (bug_risk): Improved safety check for def_load_config indexing.

The additional check on the length of def_load_config prevents out-of-range errors. Verify that this logic aligns with your intended configuration behavior for deferrable loads.

Suggested implementation:

# Improved safety check for def_load_config indexing to prevent out-of-range errors. elif len(self.optim_conf.get("def_load_config", [])) > k:

This change ensures that if "def_load_config" is not present (or is not a list), the default empty list is used so that the length check reliably prevents indexing errors. Be sure that this behavior aligns with your intended configuration handling for deferrable loads.

sourcery-ai · 2025-04-19T07:32:54Z

src/emhass/optimization.py

@@ -642,6 +688,8 @@
                })
        opt_model.constraints = constraints

+        print(repr(opt_model))


suggestion: Remove or guard debug print statements from production code.

The print(repr(opt_model)) aids debugging during development, but it might be better to remove or conditionally execute it (e.g., under a debug flag) in production.

werdnum added 3 commits November 13, 2024 14:23

Fixes for cases where the def_start_timestep, def_end_timestep and si…

bcc462b

…milar are not specified for all deferred loads (treat them as the default)

Unit tests for thermal management

1ff13e1

davidusb-geek mentioned this pull request Jan 31, 2025

Thermal Model: Optimization seems to not pick up the most cost effective hour #353

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sourcery-ai bot approved these changes Apr 19, 2025

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Thermal model updates: feasibility & unit tests #374

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