Radio Head Power Delivery Using Local Battery Peak Shaving

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Solution Overview

Problem

Current radio access network (RAN) management fails to differentiate between average and peak power consumption of radio heads, leading to increased energy costs and operational expenses due to the need for additional infrastructure to support peak power demands, which can be unsustainable and increase the carbon footprint.

Innovation Solution

A method using machine learning to manage power delivery from a local battery proximate to radio heads, differentiating between average and peak power demands by optimizing charge and discharge policies based on historical data and cost factors, allowing the local battery to handle peak power consumption while reducing the reliance on the power grid.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If site dimensioning is made based on maximum radio power consumption, then power supply capacity is sufficient, but operational cost increases significantly

Engineering Contradiction:

Improvepower supply capacityVSAvoidoperational cost

Core Design Contradiction:

ReliabilityVSUse of energy by stationary object

Solution Approach 1:

The system performs preliminary actions by charging the local battery during off-peak hours when power consumption is low and costs are reduced. The battery is pre-charged before peak demand periods occur, allowing the system to draw from stored energy during high-consumption periods without requiring oversized power supply infrastructure.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

A local battery is introduced as an intermediary energy storage device between the power grid and the radio units. This battery acts as a buffer that decouples the peak power demands from the grid connection, allowing the power supply infrastructure to be sized for average rather than peak consumption, thereby reducing operational costs.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If the number of radio units is increased to extend capacity and coverage, then service quality improves, but energy consumption increases

Engineering Contradiction:

Improvenetwork capacityVSAvoidenergy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The local battery serves as an intermediary that enables the deployment of multiple radio units without proportionally increasing grid power consumption. By providing local energy storage, the system can support additional radio units during peak periods without requiring corresponding increases in power supply capacity or operational costs.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system dynamically manages power distribution by using the local battery to supplement grid power during peak consumption periods. This dynamic approach allows the network to handle variable loads from multiple radio units efficiently, matching power delivery to actual demand rather than provisioning for maximum possible consumption.

Inventive Principle:

Principle #15Dynamics

3Productivity

If differentiation between average and peak power is implemented, then power management efficiency improves, but system complexity increases

Engineering Contradiction:

Improvepower management efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system implements self-service through automated machine learning models that independently analyze power consumption patterns, predict future demand, and manage battery charging/discharging decisions. This automation handles the complexity of differentiating between average and peak power requirements without requiring manual intervention or complex control systems.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses feedback mechanisms where machine learning models continuously monitor power consumption data, learn from historical patterns, and adjust battery management strategies accordingly. This feedback loop enables efficient differentiation between average and peak power needs while keeping system complexity manageable through adaptive, data-driven decision-making.

Inventive Principle:

Principle #23Feedback

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach reduces the number and length of power lines and fuses needed, lowers operational costs, enhances network robustness, and promotes the use of green energy, leading to cost savings and extended battery lifetime while reducing the carbon footprint of radio network operations.

Implementation Method 1

a local battery located proximate to the at least one radio head

Methodology Applied

Scientific EffectBattery (electricity): Battery (electricity)

Data Source

PatentUS20250016671A1Management of delivery of power to a radio head

Publication Date: 2025.01.09 TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

AI summary

A method performed by a computing device in a communication system for management of delivery of power to at least one radio head from a local battery located proximate to the at least one radio head is provided. The method includes determining a decision about delivery of power from the local battery to the at least one radio head for a future time window. The decision made by a machine learning model based on (i) differentiation of output power data statistics including average power and peak power demands, and (ii) time and location dependent cost data of charging and/or discharging the local battery and power grid utilization. The method further includes outputting the decision about delivery of power from the local battery to the at least one radio head for the future time window.