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

TL;DR

This paper introduces a privacy-preserving federated learning framework for multi-operator energy sharing in mobile networks, optimizing energy use and reducing costs through coordinated forecasting and scheduling.

Contribution

It presents a novel federated learning-based approach combined with optimization modules for energy sharing among mobile operators, ensuring privacy and operational efficiency.

Findings

Significant cost reductions in operational energy expenses.

Improved efficiency with increased network density.

Outperforms non-sharing baseline approaches.

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.