Real‐Time Hyperstructural Adaptive Economic Model Predictive Control for Cost‐Efficient Microgrid Operation

Residential microgrids that couple photovoltaic generation with lithium‐ion storage must curtail electricity expenditure while preserving battery health. Economic model predictive control (EMPC) is widely used for this task, yet a fixed formulation forces an unsatisfactory compromise: high‐fidelity models shrink linearization error but breach real‐time limits, whereas coarse models solve quickly at the price of missed savings when tariffs or demand shift. This study proposes a hyperstructural adaptive EMPC that reconfigures the optimization problem in real time. An intermediate‐fidelity virtual evaluation model gauges whole‐plant cost for each candidate setup; a stochastic local search then selects the most economical blend of prediction‐horizon length, time‐grid resolution, and admissible state‐of‐charge band, while a handover‐consistency constraint forces seamless trajectory transfer between successive solutions. All adaptations retain a mixed‐integer linear programming (MILP) structure, allowing deployment on ordinary embedded hardware under a strict solve‐time budget. Five‐day closed‐loop simulations on a grid‐connected household show that the adaptive scheme lowers total operating cost by about 1% and reduces capacity fade rate by roughly a factor of 2.1 relative to a static EMPC of equal baseline fidelity. Redirecting computation toward the most profitable configuration thus delivers tangible economic and durability gains for low‐voltage prosumers.