Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems.The main idea is to use the adaptive dynamic programming(ADP) technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery.Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally,simulation and comparison results are given to illustrate the performance of the presented method.

新能源电力系统不确定优化调度方法研究现状及展望

风电场和光伏电站出力的不确定性给电力系统优化调度带来很大技术挑战。文中主要介绍了考虑新能源不确定性的电力系统优化调度方法的研究现状及后续研究方向展望。首先,重点论述了各种不确定优化调度(UOD)方法,包括随机优化方法、鲁棒优化方法、随机鲁棒优化结合方法和基于人工智能技术的方法。其中,随机优化方法包括场景法、机会约束规划法和近似动态规划法;鲁棒优化方法包括传统鲁棒优化法和分布鲁棒优化法;随机鲁棒优化结合方法包括采样鲁棒优化法和分布鲁棒机会约束规划法。然后,介绍了每一种方法的优化模型形式、模型的转化和求解原理及其优缺点。最后,对UOD的后续重点研究方向进行展望,包括兼顾多个目标的UOD问题及多目标不确定优化方法、输配系统UOD问题及分布式不确定优化方法、考虑稳定性约束的UOD问题及含常微分方程约束的不确定优化方法、考虑管道传输动态的综合能源系统UOD问题及含偏微分方程约束的不确定优化方法。

Energy-Optimal and Delay-Bounded Computation Offloading in Mobile Edge Computing with Heterogeneous Clouds

By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.

基于Dueling DQN算法的列车运行图节能优化研究

通过优化地铁时刻表可有效降低地铁牵引能耗。为解决客流波动和车辆延误对实际节能率影响的问题,提出列车牵引和供电系统实时潮流计算分析模型和基于Dueling Deep Q Network(Dueling DQN)深度强化学习算法相结合的运行图节能优化方法,建立基于区间动态客流概率统计的时刻表迭代优化模型,降低动态客流变化对节能率的影响。对预测Q网络和目标Q网络分别选取自适应时刻估计和均方根反向传播方法,提高模型收敛快速性,同时以时刻表优化前、后总运行时间不变、乘客换乘时间和等待时间最小为优化目标,实现节能时刻表无感切换。以苏州轨道交通4号线为例验证方法的有效性,节能对比试验结果表明:在到达换乘站时刻偏差不超过2 s和列车全周转运行时间不变的前提下,列车牵引节能率达5.27%,车公里能耗下降4.99%。

沿海区域综合能源生产单元随机优化调度

为实现沿海区域的海上风电场、海上采气平台和陆上热电联供燃气电厂等多种能源生产子单元的协同化运行,考虑可再生能源出力和氢负荷的随机波动,提出沿海区域综合能源生产单元(coastal integrated energy production units,CIEPU)随机优化调度模型。采用参数化代价函数近似(parametric cost function approximation,PCFA)的动态规划算法求解随机优化调度模型。通过一种基于梯度下降的求解方法--Adadelta法,获得策略函数的一阶信息,并计算梯度平方的指数衰减平均值,以更新策略函数的迭代步长;对随机优化调度模型进行策略参数逼近,从而得到近似最优的策略参数,并逐一时段求解出CIEPU的最优调度计划。最后,以某个CIEPU为例,分析计算结果表明,所提出方法获得的优化调度方案可以提高CIEPU运行的经济性并降低碳排放量,验证了所提方法的准确性和高效性。

Energy-Optimal Braking Control Using a Double-Layer Scheme for Trajectory Planning and Tracking of Connected Electric Vehicles

Most researches focus on the regenerative braking system design in vehicle components control and braking torque distribution,few combine the connected vehicle technologies into braking velocity planning.If the braking intention is accessed by the vehicle-to-everything communication,the electric vehicles(EVs)could plan the braking velocity for recovering more vehicle kinetic energy.Therefore,this paper presents an energy-optimal braking strategy(EOBS)to improve the energy efficiency of EVs with the consideration of shared braking intention.First,a double-layer control scheme is formulated.In the upper-layer,an energy-optimal braking problem with accessed braking intention is formulated and solved by the distance-based dynamic programming algorithm,which could derive the energy-optimal braking trajectory.In the lower-layer,the nonlinear time-varying vehicle longitudinal dynamics is transformed to the linear time-varying system,then an efficient model predictive controller is designed and solved by quadratic programming algorithm to track the original energy-optimal braking trajectory while ensuring braking comfort and safety.Several simulations are conducted by jointing MATLAB and CarSim,the results demonstrated the proposed EOBS achieves prominent regeneration energy improvement than the regular constant deceleration braking strategy.Finally,the energy-optimal braking mechanism of EVs is investigated based on the analysis of braking deceleration,battery charging power,and motor efficiency,which could be a guide to real-time control.

虚拟电厂参与能量与辅助服务市场的协同优化策略

虚拟电厂作为一种具备储能特性的灵活性资源,虽然其在配合电网削峰填谷、消纳新能源等方面的作用越发重要,但其目前在市场运营上缺乏经济性及高效性,直接导致其参与市场积极性不足。鉴于此,提出一种虚拟电厂参与电能量市场与辅助服务市场协同工作策略。首先,基于现货市场电价与虚拟电厂的聚合情况,确定其参与市场各时段策略;其次,以虚拟电厂收益最大化为目标,采用动态规划方法构建多时间尺度下虚拟电厂参与现货电能量-调频市场协同优化模型;最后,通过仿真分析对比虚拟电厂只参与单独市场与联合优化情况下的收益及利用率,并使用shapely值法对参与虚拟电厂聚合的各类资源进行收益分摊分析,验证所提方案的可行性及有效性。

考虑灵活性不足风险的园区综合能源系统优化调度

园区综合能源系统可以实现能源的高效利用,提高可再生能源的消纳能力。但是,随着可再生能源的渗透率升高,系统的运行灵活性需求激增,给其安全、经济运行带来了巨大挑战。在这种背景下,该文提出一种考虑运行灵活性不足风险的电-热园区综合能源系统优化调度方法。首先,综合考虑电力子系统与非电(热力)子系统的运行灵活性需求,给出园区综合能源系统的运行灵活性需求和供给的数学表达,并以运行灵活性裕量和运行灵活性不足风险成本作为系统运行灵活性的评价指标。然后,基于条件风险价值,推导出系统运行灵活性不足风险约束的数学表达。由于考虑灵活性不足风险的电-热园区综合能源系统优化调度问题具有随机性、非凸性、非线性和0/1离散整数变量,在数学上属于NP-hard问题,因此,在马尔可夫决策过程框架下对其进行建模,并采用近似动态规划算法对所提模型进行求解。最后,通过算例验证了所提方法的有效性。

无人驾驶混合动力汽车轨迹跟踪节能控制融合研究

为进一步提高无人驾驶混合动力汽车轨迹跟踪精度和能耗经济性,提出了一种轨迹跟踪节能控制融合策略。首先,建立车辆运动学模型,采用模型预测控制策略对车辆进行轨迹跟踪控制;在此基础上,以速度为交互变量,提出了一种三阶段动态规划节能控制策略,在线优化最优经济性函数,以降低整车能耗总成本;最后,选择相互独立的纯跟踪轨迹跟踪算法与功率跟随节能控制策略进行比较。结果表明,所提出的轨迹跟踪节能控制融合策略提高了轨迹跟踪效果,降低了整车能耗总成本,轨迹跟踪精度提高了70.47%,纯电动和混合驱动模式下能耗总成本分别下降了4.52%和25.10%。

集中式冷热源主机单元设备级节能算法

集中式冷热源能耗在暖通空调系统中占比大,设备类型繁多,系统整体节能优化难度高,缺乏设备级节能算法支撑。对集中式冷热源设备中耗能占比高的主机设备进行研究,构建了设备级节能模型算法。目前冷热源主机能效模型算法中存在参数多、复杂度高、精确建模困难等诸多问题。在对水温及负荷率等冷热源主机运行参数对能效的影响程度研究基础上,从运行水温条件、设备负荷率两个方面综合分析,提出了基于负荷侧水温、源侧水温、源侧温差、部分负荷率等参数的设备级节能算法。该算法对于水温条件方面采用带有边界约束的解耦算法;在系统负荷分配方面采用动态规划的负荷优化分配算法。经实际项目验证,系统月度用电量平均节省率可达23.34%,节能效果显著。

基于电量电价弹性矩阵的风力发电与AA-CAES系统容量优化

考虑到系统运行经济性的影响,对基于需求侧响应(DR)的风力发电与先进绝热压缩空气储能(AA-CAES)系统协调优化问题进行了研究。首先,基于分时电价构建了用户多时段需求侧响应模型,并根据AA-CAES系统的压缩、膨胀功率建模。其次,以系统的固定成本、设备维护成本、购电成本、售电成本以及调峰成本共同构成系统总成本,采用动态规划法对AA-CAES系统的压缩、膨胀功率进行优化,求得系统总成本最小的风力发电与AA-CAES系统最优容量配置。最后,通过仿真验证所提优化方法的可行性。

火电厂一次风动态自寻优控制技术的研究与应用

国内外对燃煤电厂一次风的优化基本都是根据机组的实际情况,针对存在的控制缺陷优化原有的控制逻辑,在实际运行中,负荷变化、燃料特性的变化等因素均会影响燃煤电厂的一次风控制效果。为此,介绍了一次风动态自寻优控制技术,根据负荷、煤质及磨煤机系统特性,通过一次风动态自寻优获取最优控制,能降低各台磨煤机一次风率,减少热一次风节流损失以及一次风机出力,加快机组负荷响应,提高一次风系统自动调节品质。

Battery Energy Storage System and Demand Response Based Optimal Virtual Power Plant Operation

With certain controllability of various distribution energy resources (DERs) such as battery energy storage system (BESS), demand response (DR) and distributed generations (DGs), virtual power plant (VPP) can suitably regulate the powers access to the distribution network. In this paper, an optimal VPP operating problem is used to optimize the charging/discharging schedule of each BESS and the DR scheme with the objective to maximize the benefit by regulating the supplied powers over daily 24 hours. The proposed solution method is composed of an iterative dynamic programming optimal BESS schedule approach and a particle swarm optimization based (PSO-based) DR scheme approach. The two approaches are executed alternatively until the minimum elec-tricity cost of the whole day is obtained. The validity of the proposed method was confirmed with the obviously decreased supplied powers in the peak-load hours and the largely reduced electricity cost.

Energy Efficient Modelling of a Network

Most of the networks are generally less energy efficient and most of the time resources are underutilized. Even resources of busy networks are also underutilized and thus networks show energy inefficient management system. This paper focuses on how to obtain minimum resources for the current situation of the network to maintain connectivity, power saving and quality of service. Four different models are proposed in this perspective with different purposes and functions. These models determine the minimum resources under certain constrains. Two types of services namely, "minimum bandwidth" and "trivial file transfer" are considered. For "minimum bandwidth" service, minimum edge, minimum delay and minimum change models are proposed. Here data rate switch and enable/disable of edges are placed in these models for power saving strategy. Another model, multi flow is proposed for "trivial file transfer" service. It is proposed for transferring files through multiple flows in multiple paths from source to destination. All models except multi flow model are mixed integer programming optimization problem.

基于改进蚁群动态规划的光储微网容量优化配置

为降低并网光储微网的综合发电成本,并提高优化容量配置性能,该文研究基于改进蚁群动态规划算法的光储微网容量优化配置方法。针对传统启发式算法容易陷入局部最优以及早熟收敛的问题,首先将蚁群算法与动态规划算法结合,简化计算过程;进而,将迭代次数有关的衰减参数引入调节因子中,提高其全局搜索能力;最后,将Boltzmann选择机制引进蚁群搜索过程,并在蚁群信息素更新过程中采用偏转角度因子与拐点参数进行修正,从而大幅提高算法的优化性能。通过对海宁某小区实际数据进行仿真分析,验证了该算法的实用性和优越性。

基于动态规划法的原油管道外输系统运行能耗优化

随着油田的不断开发以及输油设备的长期运行,输油站内机泵、加热炉等耗能设备与输量产生不匹配的现象,致使外输系统整体耗能呈现逐年增涨的趋势。针对上述问题,建立以外输系统总能耗费用为目标函数的原油管道外输系统优化模型,通过外输系统热力水力计算以及约束条件确定外输系统内各站进站参数允许区间进而反算得到外输运行参数方案合集,采用动态规划法通过输油站设备能效计算对输油泵和加热炉的设备组合方案进行优化,选取当前输量下总能耗费用最低的方案作为优化的最优结果。通过此方法对某外输系统进行优化,总能耗费用降低约16%。

基于动态仿真系统的空分及高压氮气管路节能优化研究

针对国内首台整体煤气化联合循环电站,建立了关于该电站空分设备和全厂主要高压氮气使用设备及管路的实时动态仿真系统。在不同负荷下稳态运行的仿真测试表明,建立的实时动态仿真系统对于现场运行状态的仿真结果误差小于5%,表明该仿真系统具有较高的精度,能够用于对实际物理过程的仿真。利用该实时动态仿真系统对新增氮气储罐布置于原有空分至高压氮气缓冲储罐之间的管路来收集放空氮气同时平稳原有高压氮气管路压力波动的改造方案进行了研究,结果表明改造方案相比现有方案可以减少高压氮气管路系统压力波动范围,且串联布置方式优于并联布置方式。

基于并行多维近似动态规划的综合能源系统动态经济调度

针对随机动态经济调度模型难以高效求解的问题,提出一种用于快速求解电-气-热综合能源系统(IES)随机动态经济调度问题的并行多维近似动态规划算法。该算法将原有的高维状态空间中的状态变量聚合到IES的电储能系统(ESS)的可用容量和蓄热罐(HST)的可用热量中,实现了状态空间的降维,进而解决了动态规划中“维数灾”的问题。此外,利用ESS和HST的运行约束,将无效状态剔除,降低了问题的求解规模。在风电、电价和负荷等不确定场景充分训练后的值表模型中,通过近似值函数的迭代寻优,将含有经验知识的值表应用于在线测试中,实现应对不确定性的IES最优调度决策。以改进的2个系统为例,验证了所提模型和算法的有效性。

基于动态规划的无人机编队最优协同容错控制

为了克服未知的执行器故障对四旋翼无人机编队飞行的影响,提出了一种基于动态规划的最优协同容错控制律。首先,建立了四旋翼无人机模型,然后,基于动态规划设计了最优协同控制律,利用RBF神经网络逼近最优性能指标函数,设计了自适应律来估计未知的执行器故障,最终得到的最优协同容错控制律可实现对无人机编队飞行的高精度控制。通过对比仿真验证了设计的控制律具有更优的编队控制效果,编队飞行的最大轨迹跟踪误差仅为0.04 m,控制精度较高,设计的自适应律具有更优的故障估计效果,最大估计误差仅为0.05 N·m,实现了对四旋翼无人机编队的安全稳定控制。

基于动态规划算法的增程式汽车能量管理研究

针对增程式汽车存在的经济性问题,本文以运动型多用途汽车(sport utility vehicle,SUV)为例,采用动态规划算法,优化增程式汽车能量管理控制策略。给出了增程式汽车结构及主要参数,并基于AVL/Cruise模型,搭建增程式汽车关键部件模型,基于动态规划模型及动态规划的基础理论,提出增程式汽车全局动态规划控制策略求解方法,同时采用AVL Cruise中的Interface模块与Matlab/Simulink进行联合仿真。仿真结果表明,在功率跟随和动态规划两种控制策略下,功率跟随的燃油消耗为0.56 kg,动态规划控制策略的燃油消耗为0.53 kg,与功率跟随控制策略相比,燃油消耗大约减少了5.6%,经济性明显提高,说明动态规划算法的控制更加精确,效果更好,而且动力电池荷电状态(state-of-charge,SOC)大幅变化的次数少,发动机大致工作在高效区域。该研究达到了减小等效燃油消耗的目的,具有广阔的应用前景。