面向基站功率分配和能量调度的实时优化算法

Real-Time Optimization Algorithm of Power Allocation and Eenergy Scheduling for Base Station

为降低基站的购电成本,对智能电网环境下配有可再生能源和储能装置基站的功率分配和能量调度进行研究。综合考虑此类基站数据到达的随机性、信道状态的波动性、可再生能源出力的间歇性以及智能电网电价的时变性,构建受限于储能因果约束和用户最大容忍约束下的基站下行通信功率分配和能量调度模型。基于改进的Lyapunov优化理论提出了一种低复杂度实时优化算法,通过实时分配传输功率和能量调度使基站的购电成本最低,同时保证数据传输服务在用户可容忍的时延内。理论分析表明,所提算法根据当前系统状态就能进行实时决策,且优化结果无限接近最优值。仿真实验结果表明,所提算法可以有效地减少网络运营商的购电成本。

With the rapid development of mobile terminal equipment and cellular communication technology,the problem of huge increase in electricity consumption and high electricity cost becomes increasingly prominent.Targeting a base station equipped with both renewable energy source andenergy storage devices that were connected to smart grid,this paper studies the real-time optimization of power allocation and energy scheduling for downlink communication in such base station,with the goal of reducing the power purchase cost of the base station.Considering the randomness of data arrival at the base station,the fluctuation of channel state,the intermittency of renewable energy output and the time-variability of electricity price of smart grid,the power allocation and energy scheduling model of a base station in downlink communication is constructed under the constraint of energy storage causality and user maximum tolerance,and a low-complexity real-time optimization algorithm is proposed based on the improved Lyapunov optimization theory.Through real-time allocation of transmission power and energy scheduling,the power purchase cost of the base station is minimized.At the same time,the data transmission service is guaranteed within the time delay that users can tolerate.Theoretical analysis shows that the proposed algorithm can make real-time decision only according to the current system state,and the optimization result is infinitely close to the optimal value.Finally,the simulation results have evidently demonstrated that the proposed algorithm can effectively reduce the cost of electricity purchase for network operators,and the cost of electricity purchase can be reduced by 37.1%,29.8%and 15.7%,respectively,compared with the benchmark greedy algorithm.