Policy-Guided Economic MPC for Multi-Energy Microgrids with Seasonal Thermal Energy Storage

Abstract

Microgrids combining electrical and seasonal thermal energy storage offer significant operational flexibility, but their management is complicated by a severe separation of time scales: electrical components evolve over minutes, while aquifer thermal energy storage (ATES) systems charge and discharge over seasonal cycles spanning weeks to months. Conventional Economic Model Predictive Control (EMPC) cannot account for this long-term seasonal objective within a computationally tractable prediction horizon, and existing approaches close this gap through heuristic terminal costs or surrogate-derived terminal constraints, both of which require careful tuning and offer limited theoretical guarantees. This thesis investigates Policy-Guided Model Predictive Control (PG-MPC) as an alternative, in which a guiding policy embedded within the short-horizon MPC problem encodes the long-term seasonal objective, rather than relying on a precomputed reference trajectory. A combined heat and power (CHP) microgrid model, incorporating a heat pump, auxiliary boiler and chiller, battery storage, and an ATES system, is formulated using the Mixed-Logical Dynamical (MLD) framework to capture the system's discrete operational modes and nonlinear heat pump characteristics. On this shared model, a baseline EMPC controller using surrogate-derived terminal ingredients is compared against a PG-MPC controller using a simple sinusoidal dead-band guiding policy. Both controllers are benchmarked in simulation under imperfect disturbance forecasts across four scenarios spanning a range of climatological and electricity-pricing conditions, and both satisfy the end-of-year thermal balance constraint in every case. Operational cost performance is strongly scenario-dependent: PG-MPC achieves cost reductions under high price volatility and above average cooling demand, and performance comparable economically but slightly worse in terms of comfort constraint satisfaction across most scenarios. The improvement offered by PG-MPC is therefore best understood as a gain in adaptivity and robustness to realised weather and price conditions rather than a uniform cost reduction: it removes the dependence on a precomputed annual trajectory while preserving guaranteed recursive feasibility, and remains competitive with EMPC even with a deliberately simple guiding policy. The framework thus offers a tractable, theoretically grounded route to embedding seasonal storage objectives in receding-horizon microgrid control, with realised performance governed by the quality of the guiding policy and the calibration of the terminal ingredients.