Optimal Cooperative Secondary Control for Islanded DC Microgrids via a Fully Actuated Approach

DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

Distribution Control Centers in the US and Europe:Commonalities,Differences,and Lessons

The distribution control center(DCC)has evolved from a sideshow in the traditional distribution service center to a major centerpiece of the utility moving into the decentralized world.Mostly,this is the place where much of the action is happening due to new forms of energy that are coining into the distribution system.This creates the flexibility of operation and in-creased complexity due to the need for increased coordination between the transmission control center and DCC.However,the US and European utilities have adapted to this change in very different ways.Firstly,we describe the research works done in a DCC and their evolutions from the perspectives of major US utilities,and those enhanced by the European perspective focusing on the coordination of distribution system operator and transmission system operator(DSO-TSO).We pres-ent the insights into the systems used in these control centers and the role of vendors in their evolution.Throughout this paper,we present the perspectives of challenges,operational capabilities,and the involvement of various parties who will be re-sponsible to make the transition successful.Key differences are pointed out on how distribution operations are conducted between the US and Europe.

Micromagnetic studies of perpendicular recording FePt media with controllable grain size distributions

A 3-dimensional (3D) micromagnetic model combined with Fast Fourier Transform (FFT) method was built up to study the writability in the L10 FePt perpendicular medium. The effects of controllable grain size distributions were studied by grain growth simulation. It is found that the cross-track-averaged magnetization changes little between the L10 FePt medium with uniform or non-uniform grain size distribution.

Robust Consensus Tracking Control of Uncertain Multi-Agent Systems With Local Disturbance Rejection

In this paper,a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph.It is of two-degree-of-freedom nature.Specifically,a robust distributed controller is designed for consensus tracking,while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances.The condition for asymptotic disturbance rejection is derived.Moreover,even when the disturbance model is not exactly known,the developed method also provides good disturbance-rejection performance.Then,a robust stabilization condition with less conservativeness is derived for the whole multi-agent system.Further,a design algorithm is given.Finally,comparisons with the conventional one-degree-of-freedombased distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.

Differential flatness-based distributed control of underactuated robot swarms

This paper proposes a distributed control method based on the differential flatness(DF) property of robot swarms. The swarm DF mapping is established for underactuated differentially flat dynamics, according to the control objective. The DF mapping refers to the fact that the system state and input of each robot can be derived algebraically from the flat outputs of the leaders and the cooperative errors and their finite order derivatives. Based on the proposed swarm DF mapping, a distributed controller is designed. The distributed implementation of swarm DF mapping is achieved through observer design. The effectiveness of the proposed method is validated through a numerical simulation of quadrotor swarm synchronization.

Distribution Patterns of Remaining Hydrocarbons Controlled by Reservoir Architecture of Distributary Channel with Different Channel Style: S2 Formation of Songliao Basin, China

1 Introduction Reservoir architecture analysis of distributary channel of Daqing oilfield has drawn consistent interest among development geologists and petroleum engineers over the last decade(Lv et al.,1999;Zhou et al.,2008;Zhang et

Optimization of Control Loops and Operating Parameters for Three-Phase Separators Used in Oilfield Central Processing Facilities

In this study,the Stokes formula is used to analyze the separation effect of three-phase separators used in a Oilfield Central Processing Facility.The considered main influencing factors include(but are not limited to)the typical size of oil and water droplets,the residence time and temperature of fluid and the dosage of demulsifier.Using the“Specification for Oil and Gas Separators”as a basis,the control loops and operating parameters of each separator are optimized Considering the Halfaya Oilfield as a testbed,it is shown that the proposed approach can lead to good results in the production stage.

基于分布鲁棒联合机会约束的光储充电站滚动优化调控模型

随着电动汽车的普及和清洁能源装机容量的日益增加,光储充电站展现出广阔的应用前景。针对电动汽车随机接入影响下光储充电站功率难以精准调控的问题,提出基于分布鲁棒联合机会约束(DRJCC)的光储充电站滚动优化调控模型。将基于混合整数规划(MIP)的精确DRJCC模型、基于CVaR-Slim凸松弛的DRJCC模型、基于Bonferroni不等式的DRJCC模型进行对比,通过算例分析验证了所提优化调控模型的有效性。算例分析表明,所提基于CVaR-Slim凸松弛的DRJCC模型能够同时满足更多电动汽车的充电需求。

Leader-follower Optimal Selection Method for Distributed Control System in Active Distribution Networks

Aiming at the shortcomings of a traditional centralized control in an active distribution network(AND),this paper proposes a leader-follower distributed group cooperative control strategy to realize multiple operation and control tasks for an ADN.The distributed information exchange protocols of the distributed generation(DG)group devoted to node voltage regulation or exchange power control are developed using a DG power utilization ratio as the consensus variable.On these bases,this study further investigates the leader optimal selection method for a DG group to improve the response speed of the distributed control system.Furthermore,a single or multiple leader selection model is established to minimize the constraints of the one-step convergence factor and the number of leaders to improve the response speed of the distributed control system.The simulation results of the IEEE 33 bus standard test system show the effectiveness of the proposed distributed control strategy.In addition,the response speed of a DG control group can be improved effectively when the single or multiple leaders are selected optimally.

基于分布鲁棒优化的广义共享储能容量配置方法

共享储能通过储能资源的复用,能有效应对高成本和利用率低的难题。迅速发展的需求侧资源在共享储能中具有潜在应用,但其不确定性问题亟待解决。文中引入了电动汽车和温控负荷的虚拟储能模型,结合实体储能,建立了考虑不确定性的广义共享储能模型和相应的优化算法,以确定实体储能的最佳容量配置。共享储能运营商根据用户需求,实现多类型储能的优化配置,并设计虚拟储能持有者的满意度补偿,以保障其用户体验和经济利益。此外,采用Wasserstein距离描述电动汽车和温控负荷的不确定性,并结合基于风险价值的分布鲁棒机会约束算法进行求解。算例结果表明,采用广义共享储能模型和分布鲁棒机会优化算法,能够充分考虑不确定性,有效降低用户的能源消费成本和运营商的储能配置成本。

Distributed Economic MPC for Synergetic Regulation of the Voltage of an Island DC Micro-Grid

In this paper,distributed model predictive control(DMPC) for island DC micro-grids(MG) with wind/photovoltaic(PV)/battery power is proposed,which coordinates all distributed generations(DG) to stabilize the bus voltage together with the insurance of having computational efficiency under a real-time requirement.Based on the feedback of the bus voltage,the deviation of the current is dispatched to each DG according to cost over the prediction horizon.Moreover,to avoid the excessive fluctuation of the battery power,both the discharge-charge switching times and costs are considered in the model predictive control(MPC) optimization problems.A Lyapunov constraint with a time-varying steady-state is designed in each local MPC to guarantee the stabilization of the entire system.The voltage stabilization of the MG is achieved by this strategy with the cooperation of DGs.The numeric results of applying the proposed method to a MG of the Shanghai Power Supply Company shows the effectiveness of the distributed economic MPC.

Controlling the probability density of quantum systems

A new method to design a quantum controller which directly controls the probability density function（PDF） of quantum systems is proposed.Based on the quantum model from the PDF perspective,two specific control algorithms are proposed with uniform and non-uniform fields,respectively.Then a detailed control algorithm with convergence analysis is given for the small error case.By appropriately estimating the selected Lyapunov function,more accurate control effect is achieved.The proposed scheme provides a constructive method to find appropriate parameters for controller design.

Type selection and maintenance of the control system of large-scale air separation units

Based on the practice of Baosteel＇ s 60000 m3/h air separation unit （ASU） ,which is the first domestically- integrated unit of such a scale, this paper studies the principles of type selection of the distribution control system （DCS）. It discusses the design of the unit＇s control system,which involves a compressor system,a purification system （molecular sieving）, a turbo expansion system and an air separation system. The final part of the paper discusses the maintenance and future development of the ASU control system at Baosteel.

基于动态调节下垂曲线的光伏逆变器两阶段协调控制策略

在分布式光伏高渗透的有源配电网中,常规集中、局部电压控制已无法经济有效地解决电压越限、波动问题。为此提出光伏逆变器集中-局部电压两阶段控制策略,局部控制曲线采用多曲线节点灵活可调的改进无功-电压(Q-V)下垂曲线。集中控制阶段以电压控制成本与电压越限条件风险价值的加权和最小为目标函数,利用分布鲁棒优化算法和模型预测控制算法考虑光伏出力预测不确定性,对各光伏逆变器的有功功率削减量参考值和Q-V下垂曲线进行分钟级全局优化设置和调节;局部控制阶段根据Q-V下垂曲线和实时测量的节点电压,动态调整光伏逆变器无功出力。采用改进的IEEE 37节点配电系统进行仿真,结果表明:所提控制策略不仅能够集中、最优地协调各光伏逆变器的有功功率削减量和无功出力,避免电压越限,还能在局部控制阶段利用集中优化后的Q-V下垂曲线使电压波动降低40.56%,满足配电网运营商对电压控制经济性和鲁棒性的不同需求。

基于分布鲁棒模型预测控制的微电网多时间尺度优化调度

源荷多元不确定性给“源荷储”一体化微电网优化调度带来了诸多挑战,但传统方案存在优化模型过于片面极端和时间尺度单一导致调度不合理的问题,无法兼顾可靠性和经济性,并且难以协调不确定性分析方法与不同时间尺度之间的配合关系。文中基于数据驱动的多离散场景分布鲁棒方法,提出一种微电网两阶段分布鲁棒日前优化调度模型,使用列和约束生成算法进行求解。结合改进分布鲁棒优化的不确定方法、多时间尺度调度策略和模型预测控制理论,通过逐级细化调度时间尺度和减小预测周期的长度来提高精度,以最小化发电成本以及调节成本等为目标建立日前-日内多时间尺度滚动优化调度模型,较大程度上抵抗系统不确定性因素的影响。结合算例仿真分析,进一步说明了所提模型能够有效消纳新能源、降低运行成本同时兼顾安全性和经济性。

Distributed Secondary Control and Optimal Power Sharing in Microgrids

We address the control problem of microgrids and present a fully distributed control system which consists of primary controller,secondary controller,and optimal active power sharing controller.Different from the existing control structure in microgrids,all these controllers are implemented as local controllers at each distributed generator.Thus,the requirement for a central controller is obviated.The performance analysis of the proposed control systems is provided,and the finite-time convergence properties for distributed secondary frequency and voltage controllers are achieved.Moreover,the distributed control system possesses the optimal active power sharing property.In the end,a microgrid test system is investigated to validate the effectiveness of the proposed control strategies.

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

This paper considers the human-in-the-loop leader-following consensus control problem of multi-agent systems(MASs)with unknown matched nonlinear functions and actuator faults.It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory.Moreover,the leader’s input is nonzero and not available to all followers.By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults,respectively,the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed.It is proved that the state of each follower can synchronize with the leader’s state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded.Finally,simulation results are presented for verifying the effectiveness of the proposed control method.

A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden environments.The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external disturbances.The central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction topology.Toward this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance.Furthermore,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed.It is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed.Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

Distributed Secondary Control of AC Microgrids With External Disturbances and Directed Communication Topologies:A Full-Order Sliding-Mode Approach

This paper addresses the problem of distributed secondary control for islanded AC microgrids with external disturbances.By using a full-order sliding-mode(FOSM)approach,voltage regulation and frequency restoration are achieved in finite time.For voltage regulation,a distributed observer is proposed for each distributed generator(DG)to estimate a reference voltage level.Different from some conventional observers,the reference voltage level in this paper is accurately estimated under directed communication topologies.Based on the observer,a new nonlinear controller is designed in a backstepping manner such that an FOSM surface is reached in finite time.On the surface,the voltages of DGs are regulated to the reference level in finite time.For frequency restoration,a distributed controller is further proposed such that a constructed FOSM surface is reached in finite time,on which the frequencies of DGs are restored to a reference level in finite time under directed communication topologies.Finally,case studies on a modified IEEE 37-bus test system are conducted to demonstrate the effectiveness,the robustness against load changes,and the plug-and-play capability of the proposed controllers.