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Collective Grid: Privacy-Preserved Multi-Operator Energy Sharing Optimization via Federated Energy Prediction

Meysam Masoudi, Tahar Zanouda, Milad Ganjalizadeh, Cicek Cavdar

Abstract

Electricity consumption in mobile networks is increasing with the continued 5G expansion, rising data traffic, and more complex infrastructures. However, energy management is often handled independently by each mobile network operator (MNO), leading to limited coordination and missed opportunities for collective efficiency gains. To address this gap, we propose a privacy-preserving framework for automated energy infrastructure sharing among co-located MNOs. Our framework consists of three modules: (i) a federated learning-based privacy-preserving site energy consumption forecasting module, (ii) an orchestration module in which a mixed-integer linear program is solved to schedule energy purchases from the grid, utilization of renewable sources, and shared battery charging or discharging, based on real-time prices, forecasts, and battery state, and (iii) an energy source selection module which handles the selection of cost-effective power sources and storage actions based on predicted demand across MNOs for the next control window. Using data from operational networks, our experiments confirm that the proposed solution substantially reduces operational costs and outperforms non-sharing baselines, with gains that increase as network density rises in 5G-and-beyond deployments.

Collective Grid: Privacy-Preserved Multi-Operator Energy Sharing Optimization via Federated Energy Prediction

Abstract

Electricity consumption in mobile networks is increasing with the continued 5G expansion, rising data traffic, and more complex infrastructures. However, energy management is often handled independently by each mobile network operator (MNO), leading to limited coordination and missed opportunities for collective efficiency gains. To address this gap, we propose a privacy-preserving framework for automated energy infrastructure sharing among co-located MNOs. Our framework consists of three modules: (i) a federated learning-based privacy-preserving site energy consumption forecasting module, (ii) an orchestration module in which a mixed-integer linear program is solved to schedule energy purchases from the grid, utilization of renewable sources, and shared battery charging or discharging, based on real-time prices, forecasts, and battery state, and (iii) an energy source selection module which handles the selection of cost-effective power sources and storage actions based on predicted demand across MNOs for the next control window. Using data from operational networks, our experiments confirm that the proposed solution substantially reduces operational costs and outperforms non-sharing baselines, with gains that increase as network density rises in 5G-and-beyond deployments.
Paper Structure (21 sections, 11 equations, 6 figures, 2 tables)

This paper contains 21 sections, 11 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. Energy Sources
  4. Renewable Energy
  5. Electrical Grid
  6. Battery States Model
  7. Energy Infrastructure Total Cost
  8. Energy Infrastructure Sharing Framework
  9. Energy Consumption Prediction
  10. RAN Data
  11. Federated Learning
  12. FL-based Power Consumption Prediction
  13. Energy Cost Optimization Problem
  14. Infrastructure Sharing Framework
  15. Data Collection
  16. ...and 6 more sections

Figures (6)

  • Figure 1: System model for a multi-MNO area in which sites are powered either by the grid or a shared battery storage. Sites belonging to different MNOs are distinguished by color.
  • Figure 2: Multi-MNO power infrastructure sharing and battery management framework
  • Figure 3: Predicted and actual SoC based on the predicted and actual power consumption.
  • Figure 4: Cumulative total cost per site over a 15-year period for different infrastructure and battery configurations.
  • Figure 5: Annual operational cost as a function of $\zeta$, i.e., the site-sharing ratio.
  • ...and 1 more figures