Initialization Strategies for Energy Management System Optimization
F.J. Laseur (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Leo van van Iersel – Mentor (TU Delft - Discrete Mathematics and Optimization)
Gabriela Florentina Nane – Graduation committee member (TU Delft - Applied Probability)
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Abstract
The increasing integration of Renewable Energy Sources (RES), rising global electricity demand, and ongoing developments in power market structures collectively increase the complexity of Energy Management Systems (EMS). The tight scheduling of interdependent decisions in a Rolling Horizon (RH) Mixed Integer Linear Programming (MILP) environment requires efficient formulations to remain scalable and flexible to future innovations. This thesis investigates initialization strategies (warm starts) that leverage previous optimal system configurations to reduce computational complexity and solution time. Iterative cycles of variable selection, warm start execution, and problem reformulation are evaluated across multiple scenarios. These scenarios vary in modeling horizon, day-ahead price profiles, market engagement strategies, and environmental and system conditions. Problem reformulations include adjustments in the treatment of violation decision variables, linear reformulations, and the use of Benders decomposition. The results demonstrate that successful warm start implementations can substantially reduce solution times and provide valuable insights for further tightening problem formulations. Overall, the study provides guidance on efficient formulations that support effective initialization and enhance solver performance across a wide range of users and system configurations, thereby contributing to more scalable and widely applicable energy optimization practices. Keywords: Rolling Horizon (RH), Warm Start, Unit Commitment (UC), Mixed Integer Linear Programming (MILP), Benders Decomposition