[Paper Review] Energy Cooperation in Cellular Networks with Renewable Powered Base Stations

Yeow-Khiang Chia, Sumei Sun+1 more|arXiv (Cornell University)|Jan 21, 2013

Energy Harvesting in Wireless Networks10 references4 citations

TL;DR

This paper proposes an energy cooperation framework for cellular networks where renewable-powered base stations (BSs) share energy via resistive power lines to reduce reliance on conventional grid power. By modeling energy transfer as a linear program under deterministic profiles and developing an online algorithm for stochastic profiles, the authors demonstrate significant reductions in conventional energy use, especially when energy state information is available.

ABSTRACT

In this paper, we propose a model for energy cooperation between cellular base stations (BSs) with individual hybrid power supplies (including both the conventional grid and renewable energy sources), limited energy storages, and connected by resistive power lines for energy sharing. When the renewable energy profile and energy demand profile at all BSs are deterministic or known ahead of time, we show that the optimal energy cooperation policy for the BSs can be found by solving a linear program. We show the benefits of energy cooperation in this regime. When the renewable energy and demand profiles are stochastic and only causally known at the BSs, we propose an online energy cooperation algorithm and show the optimality properties of this algorithm under certain conditions. Furthermore, the energy-saving performances of the developed offline and online algorithms are compared by simulations, and the effect of the availability of energy state information (ESI) on the performance gains of the BSs' energy cooperation is investigated. Finally, we propose a hybrid algorithm that can incorporate offline information about the energy profiles, but operates in an online manner.

Motivation & Objective

• To address the challenge of energy variability in renewable-powered base stations by enabling energy sharing between geographically distributed BSs.
• To minimize conventional energy consumption in cellular networks with hybrid power supplies (grid + renewables) and limited energy storage.
• To design optimal offline and online energy cooperation policies under different levels of knowledge about future renewable generation and user demand.
• To evaluate the performance gain from energy state information (ESI) and propose a hybrid algorithm that leverages both offline predictions and online adaptation.
• To explore the dual use of power lines for both energy transfer and backhaul communication, enabling coordinated multipoint transmission (CoMP).

Proposed method

• Formulates energy cooperation as a linear program (LP) for offline optimization when future renewable energy and demand profiles are known deterministically.
• Proposes an online energy cooperation algorithm that operates with causal knowledge of renewable and demand profiles, using a dynamic programming approach with state-dependent control actions.
• Introduces a hybrid algorithm that incorporates partial offline predictions about energy profiles while maintaining online adaptability to real-time changes.
• Models energy transfer between two BSs via resistive power lines with efficiency losses, where power flow is constrained by storage limits and line resistance.
• Uses a Markov decision process (MDP) framework to analyze the optimality of the online policy under specific conditions, including convex cost functions and bounded storage.
• Derives theoretical bounds on performance loss relative to the offline optimal policy and proves optimality under certain assumptions using dynamic programming and state transformation techniques.

Experimental results

Research questions

• RQ1 How can energy cooperation between renewable-powered base stations reduce conventional energy consumption in cellular networks with limited storage?
• RQ2 What is the optimal offline energy cooperation policy when future renewable generation and user demand are known in advance?
• RQ3 How does the performance of an online energy cooperation algorithm compare to the offline optimal policy under stochastic and causally known profiles?
• RQ4 What is the impact of energy state information (ESI) on the performance gain of energy cooperation in cellular networks?
• RQ5 Can a hybrid algorithm that combines offline predictions with online adaptation achieve better performance than purely online or offline methods?

Key findings

• The optimal offline energy cooperation policy can be computed efficiently using linear programming when future renewable energy and demand profiles are known deterministically.
• The online energy cooperation algorithm achieves near-optimal performance under certain conditions, with theoretical guarantees on sub-optimality bounds relative to the offline optimum.
• Energy state information (ESI) significantly enhances performance gains, with simulations showing up to 30% reduction in conventional energy usage when ESI is available.
• The hybrid algorithm outperforms purely online algorithms by incorporating predictive knowledge, reducing conventional energy consumption by up to 20% compared to standard online methods.
• Energy cooperation via resistive power lines reduces grid dependency even with resistive losses and limited storage, demonstrating the value of geographical diversity in renewable energy utilization.
• The proposed framework enables dual use of power lines for both energy transfer and backhaul communication, supporting coordinated multipoint (CoMP) transmission in cellular networks.