Computationally efficient short-term optimization of storage-integrated thermal energy systems under uncertainty

Článek WoS

en

The increasing penetration of renewable energy sources and energy storage technologies significantly complicates the short-term operational planning of integrated energy systems, particularly those combining electricity and heat production. The presence of thermal storage, batteries, and combined heat and power units introduces strong intertemporal coupling, which rapidly increases the computational complexity of conventional optimization approaches. This paper proposes a stochastic mixed-integer linear programming framework for short-term planning of storage-integrated energy systems under uncertainty. The model is based on an energy-bus formulation, where system interactions are represented through aggregated energy balances rather than explicit network topology. The proposed formulation integrates heterogeneous components in a scalable structure while reflecting real-world operational requirements covering specific constraints and nonlinear characteristics. Uncertainty in energy prices and demand is addressed through a structured heuristic scenario reduction strategy that retains only operationally relevant trajectories, enabling fast re-optimization in a rolling-horizon context. The proposed framework is validated on two case studies reflecting real-world operating conditions in the Czech Republic: a battery-supported electricity trading system and a district heating system with combined production and thermal storage. In the analyzed scenario, the model allocated 9.2% of energy to storage for later use in heat supply, 6.8% to electricity production, and the remaining share to directly covering heat demand. The results also demonstrate that the proposed heuristic scenario reduction enables solution times on the order of seconds, while preserving key operational decisions, with deviations of approximately 1.4% in the objective function and 5% in heat supply.

Thermal energy systems with storage; Mixed-integer linear programming; Heuristic scenario reduction; Linearization techniques; Energy-bus modelling; Combined heat and power

2026-07-01

Elsevier BV

APPLIED THERMAL ENGINEERING

300

4

131509–

16