Where to Run the EMS Planner: Practical Cloud–Edge Placement for Two-Level Hierarchical EMS in Microgrids
G. Fernández(1), R. Estudillo(1), I. Sanz(1), J.F. Sanz-Osorio(2), L. Elorza-Uriarte(3) 1. Department of Electrical Systems, CIRCE Technology Centre, Zaragoza, Spain 2. Instituto Universitario de Investigación Mixto ENERGAIA (Universidad de Zaragoza—Fundación CIRCE), Zaragoza, Spain 3. Energy Storage System unit, CIDETEC (BRTA), Donostia-San Sebastián, Spain 2026-06-27

Abstract

The increasing penetration of distributed energy resources in microgrids is driving the adoption of Energy Management Systems (EMS) capable of coordinating generation, storage and flexible loads while meeting operational constraints. Two-level hierarchical EMS architectures, typically combining an upper-layer planner (rolling-horizon scheduling) with a lower-layer real-time controller, are a practical approach to cope with forecast uncertainty and fast dynamics. However, an open implementation question remains: where should the upper-layer EMS planner be executed—on the cloud, on an edge device, or in a hybrid configuration—given constraints on computation time, connectivity, and latency.

This paper proposes a lightweight cloud–edge placement tool to support this decision. The method uses a small set of measurable inputs (optimization time budget, problem size indicators, update period/granularity, and communication reliability/latency) to recommend a deployment option and to flag configurations that risk infeasibility or timeouts. The approach is validated on a representative two-level microgrid EMS workflow, comparing cloud and edge executions under different computational and communication conditions. Results show that the proposed tool correctly anticipates when edge execution becomes impractical due to solver time limits and when cloud execution may be penalized by communication constraints, providing actionable guidance for robust EMS deployment.

Key words: Microgrids, hierarchical energy management systems, cloud–edge computing, model predictive control, flexibility.

References

[1] Drgona, J.; Arroyo, J.; Figueroa, I.C.; Blum, D.; Arendt, K.; Kim, D.; Ollé, E.P.; Oravec, J.;Wetter, M.; Vrabie, D.L.; et al. All you need to know about model predictive control for buildings. Annu. Rev. Control 2020, 50, 190–232.

[2] Fang, X.; Misra, S.; Xue, G.; Yang, D. Smart Grid—The New and Improved Power Grid: A Survey. IEEE Commun. Surv. Tutor. 2012, 14, 944–980.

[3] Zhou, E.; Hale, E.; Stephen, G.; Radhakrishnan, N. Reducing Grid Costs while Abating Emissions: Opportunities for Flexible Building Loads; National Renewable Energy Laboratory (NREL): Golden, CO, USA, 2023; NREL/TP-6A40-84828.

[4] Shen, F.; Huang, S.; Wu, Q.; Repo, S.; Xu, Y.; Østergaard, J. Comprehensive Congestion Management for Distribution Networks based on Dynamic Tariff, Reconfiguration and Re-profiling Product. IEEE Trans. Smart Grid 2019, 10, 4795–4805.

[5] Strezoski, L. Distributed energy resource management systems—DERMS: State of the art and how to move forward. In Wiley Interdisciplinary Reviews: Energy and Environment; John Wiley and Sons Ltd.: Hoboken, NJ, USA, 2023; Volume 12.

[6] ENTSO-E. System Flexibility Needs for the Energy Transition: Executive Summary; ENTSO-E—European Network of Transmission System Operators for Electricity: Brussels, Belgium, 2024.

[7] Striani, S.; Unterluggauer, T.; Andersen, P.B.; Marinelli, M. Flexibility potential quantification of electric vehicle charging clusters. Sustain. Energy Grids Netw. 2024, 40, 101547.

[8] Hu, J.; Shan, Y.; Guerrero, J.M.; Ioinovici, A.; Chan, K.W.; Rodriguez, J. Model predictive control of microgrids—An overview. Renew. Sustain. Energy Rev. 2021, 136, 110422.

[9] Fernández, G.; Sanz Osorio, J.F.; Rocca, R.; Luengo-Baranguan, L.; Torres, M. Practical Considerations for the Development of Two-Stage Deterministic EMS (Cloud–Edge) to Mitigate Forecast Error Impact on the Objective Function. Appl. Sci. 2026, 16, 1844.

[10] Mayne, D.Q.; Rawlings, J.B.; Rao, C.V.; Scokaert, P.O.M. Constrained model predictive control: Stability and optimality. Autom.2000, 36, 789–814.

[11] Qin, S.J.; Badgwell, T.A. A survey of industrial model predictive control technology. Control. Eng. Pract. 2003, 11, 733–764.

[12] Hoffmann, M.; Kotzur, L.; Stolten, D.; Robinius, M. A Review on Time Series Aggregation Methods for Energy System Models. Energies 2020

[13] Hu, J.; Shan, Y.; Guerrero, J.M.; Ioinovici, A.; Chan, K.W.; Rodriguez, J. Model predictive control of microgrids—An overview. Renew. Sustain. Energy Rev. 2021, 136, 110422.

[14] Fernández, G.; Sanz Osorio, J.F.; Alarcón, A.; Torres, M.; Calavia, A. Greedy-VoI Time-Mesh Design for Rolling-Horizon EMS: Optimizing Block-Variable Granularity and Horizon Under Compute Budgets. Smart Cities 2026, 9, 30.