[Paper Review] Cooperative Energy Trading in CoMP Systems Powered by Smart Grids

Jie Xu, Rui Zhang|arXiv (Cornell University)|Mar 23, 2014

Advanced MIMO Systems Optimization18 references6 citations

TL;DR

This paper proposes a joint optimization framework for cooperative energy trading and coordinated multi-point (CoMP) transmission in smart grid-powered cellular networks, leveraging convex optimization and uplink-downlink duality to minimize total energy cost. It demonstrates significant cost reduction—up to 86% in a toy example—by enabling base stations to trade energy with the grid while jointly managing transmit precoding under QoS constraints.

ABSTRACT

This paper studies the energy management in the coordinated multi-point (CoMP) systems powered by smart grids, where each base station (BS) with local renewable energy generation is allowed to implement the two-way energy trading with the grid. Due to the uneven renewable energy supply and communication energy demand over distributed BSs as well as the difference in the prices for their buying/selling energy from/to the gird, it is beneficial for the cooperative BSs to jointly manage their energy trading with the grid and energy consumption in CoMP based communication for reducing the total energy cost. Specifically, we consider the downlink transmission in one CoMP cluster by jointly optimizing the BSs' purchased/sold energy units from/to the grid and their cooperative transmit precoding, so as to minimize the total energy cost subject to the given quality of service (QoS) constraints for the users. First, we obtain the optimal solution to this problem by developing an algorithm based on techniques from convex optimization and the uplink-downlink duality. Next, we propose a sub-optimal solution of lower complexity than the optimal solution, where zero-forcing (ZF) based precoding is implemented at the BSs. Finally, through extensive simulations, we show the performance gain achieved by our proposed joint energy trading and communication cooperation schemes in terms of energy cost reduction, as compared to conventional schemes that separately design communication cooperation and energy trading.

Motivation & Objective

• To address rising operational costs in dense cellular networks by integrating renewable energy and two-way energy trading with smart grids.
• To overcome the limitations of conventional CoMP designs that ignore dynamic energy pricing and renewable supply variability.
• To jointly optimize energy trading and precoding in CoMP systems to minimize total energy cost while meeting QoS requirements.
• To develop both optimal and suboptimal solutions for practical deployment, with low-complexity ZF-based precoding as a scalable alternative.

Proposed method

• Formulates a joint optimization problem minimizing total energy cost via energy trading and CoMP precoding under QoS and power constraints.
• Applies uplink-downlink duality to transform the non-convex downlink problem into a dual uplink problem amenable to convex optimization.
• Uses Lagrangian relaxation and subgradient methods with the ellipsoid method to solve the dual problem iteratively.
• Derives optimal energy trading and precoding via Karush-Kuhn-Tucker (KKT) conditions and dual decomposition.
• Proposes a suboptimal ZF-based precoding scheme to reduce computational complexity while maintaining performance.
• Validates the approach through simulations comparing joint optimization against conventional separate design of CoMP and energy trading.

Experimental results

Research questions

• RQ1 How can energy trading and CoMP transmission be jointly optimized to minimize total energy cost in smart grid-powered cellular networks?
• RQ2 What is the performance gain of joint optimization compared to conventional CoMP or isolated energy trading designs?
• RQ3 How does the inclusion of two-way energy trading with variable grid prices affect system cost and energy efficiency?
• RQ4 What is the trade-off between optimality and complexity in energy and precoding management under realistic constraints?
• RQ5 How does renewable energy variability across base stations impact the effectiveness of cooperative energy trading?

Key findings

• The proposed joint optimization reduces total energy cost by 86% in a toy example compared to conventional CoMP design, from 0.356 to 0.05 units.
• The optimal solution achieves cost minimization through coordinated energy trading and precoding, with exact energy purchase/sale levels derived from KKT conditions.
• The suboptimal ZF-based scheme reduces computational complexity while maintaining strong performance, making it suitable for real-time deployment.
• Energy cost savings are significantly higher when cooperation accounts for both energy trading and communication coordination, rather than treating them separately.
• The system achieves substantial cost reduction even with heterogeneous renewable generation and variable grid pricing across base stations.