考虑5G基站储能可调度容量的有源配电网协同优化调度方法

Coordinated Optimization Scheduling for Active Distribution Networks Considering Schedulable Capacity of Energy Storage for 5G Base Stations

随着移动通信向5G快速更新换代,5G基站建设规模快速增长,可将海量5G通信基站中的闲置储能视作灵活性资源参与电力系统调度,以减轻新能源发电的随机性和波动性对系统的不利影响。针对含分布式风力发电有源配电网的基站储能经济优化调度问题,首先计及配电网潜在电力中断以及停电恢复时间2个因素,建立基站可靠性评估模型,系统地评估各基站储能的实时可调度容量。进一步以最小化系统运行成本为目标,采用基于变分自编码器(variational auto-encoder,VAE)模型的改进双延迟深度确定性策略梯度(twin delayed deep deterministic policy gradient,TD3)算法求解5G基站储能最优充放电策略。该算法将多基站储能状态用隐变量的形式表征以挖掘数据中隐含的关联,从而降低模型的求解复杂度,提升算法性能。通过迭代求解至收敛,实现多基站储能(multi-base station energy storage,MBSES)系统的实时调控并为每个基站制定符合实际工况的个性化充放电策略。最后通过算例验证了所提方法的有效性。

With the rapid deployment of 5G mobile communication,the construction of numerous 5G base stations offers significant flexibility resources for the power system.By utilizing the spare stored energy of these 5G base stations as schedulable energy storage resources,the adverse effects of wind power generation's randomness and volatility on the system can be mitigated.The optimization scheduling problem of active distribution networks containing distributed wind power generation is the focus of this article.Firstly,a base station reliability evaluation model is established to systematically evaluate the real-time schedulable capacity of the base station energy storage based on two factors:the potential power interruption and the power outage recovery time.Furthermore,an improved Twin Delayed Deep Deterministic policy gradient(TD3)algorithm,based on a Variational Autoencoder(VAE)model,is utilized to minimize the system operation cost by solving the optimal charging and discharging strategy of the 5G base station energy storage.The energy storage status of multiple base stations is represented in the form of hidden variables to mine the hidden associations in the data,thereby reducing the model solving complexity and improving the algorithm performance.By iteratively solving to converging,the multi-base station energy storage(MBSES)system achieves continuous action control,and the personalized charging and discharging strategies tailored to the actual operating conditions are developed for each base station.Finally,the effectiveness of the proposed method is verified through numerical examples.