Add comprehensive tests for DEMS.py to improve code coverage

[Copilot]

This PR adds comprehensive unit tests for the hydesign/HiFiEMS/DEMS.py module to achieve coverage on previously untested lines, specifically targeting lines 57, 61, 103-106, 119-120, 154-167, 273, 286, 313-320, 374-615, 676-940, 982-1081, 1134-1357, and 1374-1395.

Changes Made

New Test File: hydesign/tests/test_dems.py

The new test suite includes:

ReadData Function Coverage

• Tests for missing measurement columns in wind/solar data (lines 57, 61)
• Tests for edge cases in market data processing when regulation prices equal spot market prices (lines 103-106)
• Tests for the BP=2 market configuration condition (lines 119-120)

Utility Function Coverage

• Complete test coverage for f_xmin_to_ymin() function with different time resolution conversions (lines 154-167)
• Tests for get_var_value_from_sol() function for extracting optimization variable values (lines 172-179)
• Tests for revenue_process() function for processing revenue calculation results (lines 1374-1395)

Pandas Compatibility

• Added compatibility layer for deprecated pandas.DataFrame.append() method used in legacy code
• Implemented context manager for clean pandas patching during tests

Coverage Results

Successfully achieved coverage on the following target lines:

• ✅ Lines 57, 61: ReadData edge cases for missing measurement data
• ✅ Lines 103-106: ReadData market data equal price condition
• ✅ Lines 119-120: ReadData BP=2 configuration
• ✅ Lines 154-167: Complete f_xmin_to_ymin function
• ✅ Lines 1374-1395: Complete revenue_process function

Note on Optimization Functions: Lines 273, 286, 313-320 (SMOpt), 374-615 (BMOpt), 676-940 (RDOpt), 982-1081 (RTSim), and 1134-1357 (RBOpt) require the CPLEX optimization solver which is not available in the CI environment. These functions are complex mathematical optimization routines that would need integration tests with proper CPLEX licensing to achieve meaningful coverage.

Testing Infrastructure

The tests follow existing repository patterns and include:

• Proper test data setup with realistic forecast and market data
• Mock objects for testing utility functions
• Error handling and edge case validation
• Integration with pytest and coverage reporting

Total coverage increased from ~17% to 21% for the DEMS.py module, successfully covering all feasible target lines given the CPLEX dependency constraints.

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