Optimal configuration of 5G base station energy storage considering sleep mechanism

The high-energy consumption and high construction density of 5G base stations have greatly increased the demand for backup energy storage batteries.To maximize overall benefits for the investors and operators of base station energy storage,we proposed a bi-level optimization model for the operation of the energy storage,and the planning of 5G base stations considering the sleep mechanism.A multi-base station cooperative system composed of 5G acer stations was considered as the research object,and the outer goal was to maximize the net profit over the complete life cycle of the energy storage.Furthermore,the power and capacity of the energy storage configuration were optimized.The inner goal included the sleep mechanism of the base station,and the optimization of the energy storage charging and discharging strategy,for minimizing the daily electricity expenditure of the 5G base station system.Additionally,genetic algorithm and mixed integer programming were used to solve the bi-level optimization model,analyze the numerical example test comparison of the three types of batteries and the net income of the configuration,and finally verify the validity of the model.Furthermore,the sleep mechanism,the charging and discharging strategy for energy consumption,and the economic benefits for the operators were investigated to provide reference for the 5G base station energy storage configuration.

Energy efficient and delay aware ternary-state transceivers for aerial base stations

In recent times, Aerial Base Stations(AeBSs) are being investigated to provide wireless coverage to terrestrial radio terminals. There are many advantages of using aerial platforms to provide wireless coverage, including larger coverage in remote areas and better line-of-sight conditions, etc. Energy is a scarce resource for the AeBSs, hence the wise management of energy is quite beneficial for the aerial network. In this context, we study the means of reducing the total energy consumption by designing and implementing an energy efficient AeBSs as presented in this paper. Implementing the sleep mode in the Base Stations (BSs) has been proven to be a very good approach for improving the energy efficiency and we propose a novel strategy for further improving energy efficiency by considering ternary state transceivers for AeBSs. Using the three state model, we propose a Markov Decision Process (MDP) based algorithm, which intelligently switches among three states of the transceivers based on the offered traffic meanwhile maintaining a prescribed minimum channel rate per user. We define a reward function for the MDP, which helps us to get an optimal policy for selecting a particular mode for the transceivers of the AeBS. Considering an AeBS with transceivers whose states are changeable, we perform simulations to analyse the performance of the algorithm. Our results show that, compared with the always active model, around 40% gain in the energy efficiency is achieved by using our proposed MDP algorithm together with the three-state transceivers model. We also show the energy-delay tradeoff in order to design an efficient AeBS.

Optimal configuration for photovoltaic storage system capacity in 5G base station microgrids

Base station operators deploy a large number of distributed photovoltaics to solve the problems of high energy consumption and high electricity costs of 5G base stations.In this study,the idle space of the base station’s energy storage is used to stabilize the photovoltaic output,and a photovoltaic storage system microgrid of a 5G base station is constructed.Aiming at the capacity planning problem of photovoltaic storage systems,a two-layer optimal configuration method is proposed.The inner layer optimization considers the energy sharing among the base station microgrids,combines the communication characteristics of the 5G base station and the backup power demand of the energy storage battery,and determines an economic scheduling strategy for each photovoltaic storage system with the goal of minimizing the daily operation cost of the base station microgrid.The outer model aims to minimize the annual average comprehensive revenue of the 5G base station microgrid,while considering peak clipping and valley filling,to optimize the photovoltaic storage system capacity.The CPLEX solver and a genetic algorithm were used to solve the two-layer models.Considering the construction of the 5G base station in a certain area as an example,the results showed that the proposed model can not only reduce the cost of the 5G base station operators,but also reduce the peak load of the power grid and promote the local digestion of photovoltaic power.

计及构网型储能电站的新能源基地无功协调优化控制

与传统火电主导的系统相比,缺乏同步电源支撑的新能源基地呈现低抗扰性和弱支撑性,采用构网型储能电站(GFMS)是一种有效的解决方案。文中首先对GFMS控制原理进行分析,确定了稳态及动态条件下GFMS的功率调节范围,并建立了GFMS运行模型。然后,提出了考虑GFMS辅助治理的无功协调优化控制策略,以适应无功协调优化及实时电压支撑需求。在无功协调优化阶段,以经济性最优和动态无功预留容量最大为目标,对无功源进行日前-日内集中优化;在实时电压支撑阶段,提出了一种基于并网点电压支撑需求的GFMS电压控制策略。选取中国西北地区某缺乏同步电源支撑的新能源基地为算例进行仿真,验证所提策略的有效性。结果表明,所提无功协调优化控制策略有效提高了新能源基地安全稳定水平,充分发挥了GFMS的作用。

Temporal and Spatial Optimization for 5G Base Station Groups in Distribution Networks

With the large-scale connection of 5G base stations(BSs)to the distribution networks(DNs),5G BSs are utilized as flexible loads to participate in the peak load regulation,where the BSs can be divided into base station groups(BSGs)to realize inter-district energy transfer.A Stackelberg game-based optimization framework is proposed,where the distribution net-work operator(DNO)works as a leader with dynamic pricing for multi-BSGs;while BSGs serve as followers with the ability of demand response to adjust their charging and discharging strategies in temporal dimension and load migration strategy in spatial dimension.Subsequently,the presence and uniqueness of the Stackelberg equilibrium(SE)are provided.Moreover,differential evolution is adopted to reach the SE and the optimization problem in multi-BSGs is decomposed to solve the time-space coupling.Finally,through simulation of a practical system,the results show that the DNO operation profit is increased via cutting down the peak load and the operation costs for multi-BSGs are reduced,which reaches a winwin effect.

雅砻江流域水风光多能互补优化运行研究

雅砻江清洁能源基地的水电、风电、光伏、抽水蓄能规划装机容量达7 800万kW,有必要开展水风光多能互补优化运行以获得最佳效益。在对水、风、光、蓄电源进行精细化模拟基础上,以水风光互补总收益最大和剩余负荷峰值最小为目标,构建水、风、光、蓄多能互补运行模型,根据模型特点,提出采用混合整数线性规划方法进行求解,旨在为雅砻江流域多能互补优化运行提供可借鉴的方法。

AEECA for Reliable Communication to Enhance the Network Life Time for WSN

Nowadays,wireless sensor networks play a vital role in our day to day life.Wireless communication is preferred for many sensing applications due its convenience,flexibility and effectiveness.The sensors to sense the environmental factor are versatile and send sensed data to central station wirelessly.The cluster based protocols are provided an optimal solution for enhancing the lifetime of the sensor networks.In this paper,modified K-means++algorithm is used to form the cluster and cluster head in an efficient way and the Advanced Energy-Efficient Cluster head selection Algorithm(AEECA)is used to calculate the weighted fac-tor of the transmission path and effective data collection using gateway node.The experimental results show the proposed algorithm outperforms the existing routing algorithms.

考虑5G基站备用储能优化调控的配电网重构双层优化方法

5G基站建设数量激增带来的大量负荷影响了配电网的运行经济性,但基站备用储能在保证可靠性的动态备电前提下能够参与削峰填谷,如何利用配电网重构和储能调控提高双方运行经济性亟待研究。该文首先分析了5G基站备用储能的调控潜力,建立了基站储能可调控容量模型。针对配电网不同拓扑结构的节点可靠性,利用序贯蒙特卡洛法进行评估,提出了考虑节点可靠性的基站储能备电时长确定方法。在此基础上,构造了考虑5G基站备用储能优化调控的配电网重构双层优化模型。通过算例说明,该文提出的重构模型考虑了基站备用储能削峰填谷,使配电网运行经济性更高;重构提高了节点的可靠性,使基站储能拥有更大的调控空间,有效地降低了基站运行成本,实现了双方的互利共赢。

考虑5G基站储能参与配电网供电恢复研究

5G的快速发展使得5G基站储能容量大幅增加,如何充分发挥时常处于休眠状态的基站储能资源,使其参与配电网供电恢复,降低配电网失电量问题亟待研究。该文通过协调基站储能参与配电网供电恢复,以发挥其储能价值。首先,建立风光联合出力场景集,通过赤池信息准则和平方欧式距离确定各时段最优Copula函数,进而对其进行抽样聚类,获得风光典型出力场景集;然后,建立基站备用储能模型,通过正弦函数叠加建立不同区域的通信量模型,利用泰尔熵和修正基尼系数建立综合脆弱度模型确定各基站储能备用时间及备用储能模型,进而获得不同基站储能的可调度容量;最后,以失负荷量损失最小为目标,建立考虑基站储能参与及风光出力波动的含可调参数的两阶段鲁棒优化模型。以改进的IEEE 33节点模型为例进行仿真验证,分别对比了定基站备用时间与该文所提方法确定备用时间、鲁棒优化与确定性优化等方案下系统失负荷量的变化情况。仿真结果表明,5G基站储能可降低失负荷量损失成本,提高风光消纳率。

基于负荷准线的5G基站虚拟电厂优化控制方法

在新型电力系统中,亟待深度挖掘需求侧资源以提升系统灵活性和新能源消纳能力。在“新基建”背景下,5G基站作为一种新型需求侧资源正迅速发展。研究如何在保证基站备用需求的前提下,由铁塔公司组建含大规模5G基站的虚拟电厂(virtual power plant,VPP)并常态化参与需求响应。首先,提出了考虑储能动态备用容量的5G基站运行可行域构建方法,建立了5G基站VPP的聚合模型。然后,建立了5G基站VPP响应负荷准线的日前优化模型,提出了适合对大规模5G基站进行协调控制的日内解聚合方法。最后,建立了含高比例新能源的区域电网仿真算例。仿真结果表明,聚合大规模基站参与准线型需求响应,可以显著降低5G基站的运行成本,同时提高电网的新能源消纳能力。

含备用储能的5G基站虚拟电厂参与电力市场的交易策略研究

随着第五代移动通信(5th generation mobile communication,5G)基站建设数量的剧增,5G基站的备用电池将是一个具有巨大潜能的储能资源。我国电力市场化改革正不断推进,研究5G基站的备用储能参与电力市场交易是实现电网和通信运营商互利共赢的有效途径。考虑到5G基站备用储能参与电力市场的特殊性,建立了计及退化成本和可调度容量的基站储能电池模型,并将零散的基站聚合成5G基站虚拟电厂(virtual power plant,VPP)。然后,将5G基站VPP作为独立的竞价主体,构建双层的电能量-调频辅助服务市场交易决策模型,并利用KKT(Karush-Kuhn-Tucker)条件和对偶理论将双层模型转化为可解的混合整数规划问题。最后,通过算例评估了5G基站备用储能的可调度容量以及所提交易决策模型的有效性。

基于Floyd算法的5G基站区域储能分配策略

由于5G集中在人群密集区域配置,需要的基站数量多且能耗大,因此,运营商需要支付的购电成本很多。为了降低运营商的购电成本,本文提出一种基于Floyd算法的5G基站区域储能分配策略。首先,将供电网络中的5G基站区域储电站等效为一系列节点,并根据节点之间的距离信息计算出传输损失率矩阵;其次,对各节点的储能情况进行分析,并根据其储能情况将节点分为供能节点与需求节点;最后,基于Floyd算法来合理调配该储能网络中的能量。仿真结果表明,在不引入外部供电和引入外部供电两种情况下,本文所提出的系统在运行2 a或150 d后分别达到稳定状态,系统不再需要额外从公用电网购电,减轻了电网供电压力的同时,降低了运营商的购电成本。

考虑负荷时空迁移的5G基站与配电网协同优化运行

快速增长的5G基站电力负荷给通信运营商及城市配电网带来了挑战。合理利用5G基站负荷的时空灵活性特征,可以降低基站用电成本以及缓解配电网供电压力。围绕5G基站负荷的时空灵活性特征,分析基站负荷通过时空迁移参与需求响应的潜力;基于负荷时间迁移角度的基站备用储能调度及空间迁移角度的基站休眠手段,以基站与配电网总运行成本最低为目标,建立5G基站与配电网协同优化运行模型。算例分析结果表明,5G基站基于时空灵活特征参与配电网协同优化运行,可以有效降低基站的功耗及运营成本,提高配电网新能源消纳率,促进电网经济、可靠运行。

计及5G基站运行协同的分布式储能优化配置方法

通信基站普遍配有备电储能,且其大多数时间处于闲置状态。随着5G基站的加速建设,与之而来的大量备电储能对配电网来说将是十分可观的灵活性资源。为实现资源的充分利用,探究5G基站储能参与配电网灵活调控的潜力具有重要意义。在此背景下,该文提出一种计及5G基站储能协同的分布式储能(DES)优化配置方法:首先,通过对5G基站的负荷特性及灵活储能容量进行分析,提出含5G基站储能的配电网“规划-运行”双层优化模型,其中,规划层以网络年综合成本最小为目标,对DES进行选址定容,运行层以电压综合水平最优为目标,对DES与基站储能的调度策略进行寻优;然后,为高效求解该模型,提出了一种适用于大量异质变量的多目标动态评价优化算法,该算法通过对变量分组并在求解过程中自适应调整目标函数的选取,以加快求解速度、提高搜索精度;最后,仿真结果证明了所提出的DES协同优化配置方法在降低成本和促进新能源消纳方面的有效性,以及所提出优化算法在兼顾多目标与快速求解方面的优越性。

面向配电网削峰填谷的5G基站储能调控方法

针对现有5G基站储能参与配电网调控所面临的集中调控方法计算复杂度高、难以适配配电网负荷波动、无法感知基站储能电池能量与电池损耗等挑战,提出5G基站储能分布式自主调控架构,支撑5G分布式储能资源灵活接入配电网。在此基础上,提出一种基于电池状态感知上置信区间的5G基站储能调控算法,综合考虑基站储能电池容量约束与电池损耗成本,最大化储能调控效益和配电网负荷曲线方差的加权差,实现电池能量与电池损耗感知,辅助配电网削峰填谷。仿真结果表明:相较于现有2种对比算法,所提方法可以使基站储能调控效益与配电网负荷曲线方差的加权差分别提高12.73%和22.33%。研究成果能够很好地满足5G基站储能参与配电网削峰填谷的需求,实现配电网与5G基站储能双向互动。

5G通信基站光储配置及充放电方法的研究

5G通信基站作为信息通信领域的基础设施,满足了人们对日常通话和流量使用的基本需求。为响应国家“3060”双碳战略,通信基站需要改变传统的电网供电模式,引进光伏系统和储能电池等绿色能源,实现“绿色、节能、低碳”发展。在不同的电力条件下,如何规划光伏和储能配置已成为信息和通信领域的一个重要研究方向。创新模拟光伏函数模型,探索不同电力条件下光储配置的最优方法,创新光储系统的充放电方法,实现绿色能源应用的效益最大化。

含5G基站的配电台区柔性互联协调规划策略

为解决高比例分布式电源接入配电台区出现的区域源荷分布不均、新能源出力过剩以及电压波动等问题,提出了含5G基站的配电台区柔性互联协调规划策略。首先,建立了含5G基站及其后备储能的配电台区柔性互联系统模型。然后,构建3层协调规划模型,上层模型以配电网电压综合水平最优为目标,中层模型以配电台区综合运行成本最小为目标,分别用于决策台区柔性互联配电网供电可靠性和电压源换流器容量配置;下层模型以5G基站集群的综合成本最小为目标,采用遗传算法和库恩塔克(Karush-Kuhn-Tucker,KKT)条件相结合的混合算法求解,以实现系统优化调度。最后,以IEEE33节点配电网作为算例进行仿真验证,验证了所提方法的有效性和准确性。

计及参与调峰辅助服务的智慧基站光储系统优化运行策略研究

随着5G基站建设不断推广,基站能耗大、成本高的问题备受关注。通过光储系统的配置和优化运行,并进一步参与调峰辅助服务,可有效提升5G基站用能的经济性,同时实现绿色能源在5G基站中的规模应用。因此,针对5G基站光储系统参与电网调峰辅助服务的经济性开展了研究。首先,根据基站负荷特性和不同调峰时段建立了考虑光储系统参与调峰辅助服务的优化目标模型;然后,采用CPLEX优化求解工具进行优化模型的求解,获取储能的最优容量和运行策略;最后,构建了储能单独参与调峰、光储联合参与调峰等场景,通过算例分析了不同调峰模式对储能的优化配置容量和5G基站用能成本有较大的影响,可为5G智慧基站光储系统参与调峰辅助服务的优化运行提供了参考。

通信站点储能降费应用探索与实践

随着5G站点的规模部署,通信站点功耗大幅增长带给运营商巨大的电费成本支出压力,节电降费迫在眉睫。一般工商业峰谷电价差显著且浮动比例将进一步扩大,通过储能技术可实现电费运营成本的大幅下降。本文重点介绍了室外机柜站点储能技术原理、实践效果及储能运营创新模式,为通信站点的储能降费提供新思路。

5G基站的负荷预测和储能调度技术分析

阐述基于5G基站负荷的不确定性和储能技术的应用,提出一种综合的负荷预测与调度系统。通过数据分析和机器学习模型构建负荷预测模型,结合储能装置的选择和调度优化策略,实现对5G基站负荷的有效管理与优化。