Greedy-VoI Time-Mesh Design for Rolling-Horizon EMS: Optimizing Block-Variable Granularity and Horizon under Compute Budgets

Rolling horizon energy management systems (EMS) and model predictive control (MPC) for microgrids in smart cities face a fundamental trade-off: finer temporal discretization improves operational characteristics but rapidly increases the size of the optimization problem and execution time, jeopardizing real-time feasibility. Furthermore, in short-horizon operation, only the first control actions are implemented, while long-horizon decisions primarily guide feasibility and constraints. This paper proposes a computation-aware temporal mesh design layer that jointly selects a variable granularity of blocks and an optimisation horizon, explicitly bounded by market-aligned settlement steps and per-cycle computation budgets. Candidate configurations are represented as pairs ⟨B, H⟩, where B is a constant-pass block programme, and H is the horizon, and are uniquely tracked through an auditable mesh signature. The method first evaluates a predefined, market-consistent set to establish reproducible cost and execution time benchmarks, then applies a Greedy-Value-of-Information (Greedy-VoI) search that generates valid neighbouring meshes through local refinement, thickening, and resolution reallocation without breaking the horizon mosaic or admissible step sizes. All candidates are evaluated using the same microgrid use case and the same comparative KPIs, enabling the systematic identification of near-optimal mesh-horizon designs for practical EMS implementation.