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.      

基于边缘计算的混合储能多微电网功率自适应控制

在微电网中,将多个双向接口变换器并联可提高微电网两侧的功率交换能力,但并联变换器的微电网难以有效控制。研究发现,微电网接入储能系统可以缓冲功率波动、降低功率控制难度。为保证微电网稳定运行,提高功率控制精度,本文提出了基于边缘计算的混合储能多微电网功率自适应控制方法。研究基于边缘计算技术,分析了混合储能多微电网结构,并构建了一个用于混合储能多微电网的自动管理平台。在此基础上,通过对母线实时电压的控制实现频率的调节,增强接口变换器的稳定性,以完成混合储能多微电网功率交互的调度与控制。在混合微电网中,我们接入储能系统,通过功率交换控制、自主功率分频自适应控制实现了混合储能多微电网的功率自适应控制。实验结果表明,所提方法在混合储能多微电网功率控制方面效果较好,有功功率控制误差较小,能够有效保证混合储能多微电网稳定运行,并提高功率自适应控制准确度。     

LMI‐Based Robust PID Controller Design for Voltage Control of Islanded Microgrid

This paper presents the design of a robust proportional integral derivative (PID) controller for the control of a single phase microgrid voltage. A microgrid consists of loads, distributed generation units and several power‐electronics interfaced LC filter and voltage source inverter. These loads are unknown and parameters are uncertain which produce unmodeled load dynamics. This unmodeled load dynamics reduces the voltage tracking performance of the microgrid. The proposed controller gives the robustness of the system with unmodeled load dynamics. Under different kinds of uncertainties, PID controller guarantees the stability and provides zero steady‐state error and fast transient response. The robustness and optimal performance of the controller is obtained by using linear matrix inequality approach. The performance of the controller under different uncertainties is studied. Results indicate the robustness and high voltage tracking performance of the microgrid system.     

微电网系统建模的挑战—“智能微电网与可再生能源系统的关键技术”专题(前言)

<正>随着可再生和绿色分布式发电系统渗透率的不断提高,含高渗透率分布式可再生能源的微电网成为智能电网发展的重要分支之一.微电网系统是由负荷、分布式电源、电力电子变换器通过电气网络紧密集成的可控供电系统,逐渐成为我国城镇化进程中重要的一种供电模式.典型微电网系统通过电力电子变换器接入多种分布式电源,其中风、光等分布式可再生能源接入比例较大,这些发电单元大幅度随机变化、各单元动态特性各异、不同单元的变化呈现多个时间尺     

Distributed control and energy storage requirements of networked Dc microgrids

Microgrids are a key technology to help improve the reliability of electric power systems and increase the integration of renewable energy sources. Interconnection and networking of smaller microgrids into larger systems have potential for even further improvements. This paper presents a novel approach to a distributed droop control and energy storage in networked dc microgrids. Distributed control is necessary to prevent single points of failure along with flexibility and adaptability to changing energy resources. The results show that systems with random sources and fast update rates, a networked microgrid structure can minimize required energy storage requirements.     

基于分布式深度强化学习的微电网实时优化调度

随着海量新能源接入到微电网中, 微电网系统模型的参数空间成倍增长, 其能量优化调度的计算难度不断上升. 同时, 新能源电源出力的不确定性也给微电网的优化调度带来巨大挑战. 针对上述问题, 本文提出了一种基于分布式深度强化学习的微电网实时优化调度策略. 首先, 在分布式的架构下, 将主电网和每个分布式电源看作独立智能体. 其次, 各智能体拥有一个本地学习模型, 并根据本地数据分别建立状态和动作空间, 设计一个包含发电成本、交易电价、电源使用寿命等多目标优化的奖励函数及其约束条件. 最后, 各智能体通过与环境交互来寻求本地最优策略, 同时智能体之间相互学习价值网络参数, 优化本地动作选择, 最终实现最小化微电网系统运行成本的目标. 仿真结果表明, 与深度确定性策略梯度算法(Deep Deterministic Policy Gradient, DDPG)相比, 本方法在保证系统稳定以及求解精度的前提下, 训练速度提高了17.6%, 成本函数值降低了67%, 实现了微电网实时优化调度.     

环形直流微电网的二次控制与稳定性分析

本文阐述了一种环形直流微电网系统分布式二次控制的稳定性分析方法,实现了微网系统的电压调控和电流分配.首先,借助多智能体系统的一致性算法,设计了局部观测器来估计所有分布式能源节点的平均电压.然后,基于观测器状态和邻居节点的电流信息设计了融合误差和动态反馈控制器,并通过解耦潮流代数方程得到了关于融合误差的闭环系统.进一步基...     

Distributed generation system control strategies with PV and fuel cell in microgrid operation

Control strategies of distributed generation (DG) are investigated for different combination of DG and storage units in a microgrid. In this paper the authors proposed a microgrid structure which consists of a detailed photovoltaic (PV) array model, a solid oxide fuel cell (SOFC) and various loads. Real and reactive power (PQ) control and droop control are developed for microgrid operation. In grid-connected mode, PQ control is developed by controlling the active and reactive power output of DGs in accordance with assigned references. Two PI controllers were used in the PQ controller, and a novel heuristic method, artificial bee colony (ABC), was adopted to tune the PI parameters. DGs can be controlled by droop control both under grid-connected and islanded modes. Droop control implements power reallocation between DGs based on predefined droop characteristics whenever load changes or the microgrid is connected/disconnected to the grid, while the microgrid voltage and frequency is maintained at appropriate levels. Through voltage, frequency, and power characteristics in the simulation under different scenarios, the proposed control strategies have demonstrated to work properly and effectively. The simulation results also show the effectiveness of tuning PI parameters by the ABC.     

Multi-objective voltage and frequency regulation in autonomous microgrids using Pareto-based Big Bang-Big Crunch algorithm

Voltage and frequency regulation is one of the most vital issues in autonomous microgrids to ensure an acceptable electric power quality supply to customers. In this paper, a real-time control structure including power, voltage, and current control loops is proposed for microgrid inverters to restore voltage and frequency of the system after the initiation and load changes. The Proportional-Integral (PI) gains of the voltage controller are optimized in a real-time basis after a perturbation in the microgrid to have a fast and smooth response and a more stable system. The current controller produces Space Vector Pulse Width Modulation command signals to be fed into the three-leg inverter. The multi-objective optimization problem has objective functions of voltage overshoot/undershoot, rise time, settling time, and Integral Time Absolute Error (ITAE). The modified Multi-Objective Hybrid Big Bang-Bing Crunch (MOHBB-BC) algorithm is employed as one of efficient evolutionary algorithms in order to solve the optimization problem. The MOHBB-BC method obtains a set of Pareto optimal solutions; a fuzzy decision maker is used to pick up the most preferred Pareto solution as the final solution of the problem. Results from testing the control strategy on a case study are discussed and compared with previous works; according to them, the proposed method is able to obtain dynamic PI regulator gains to have a more appropriate response.     

Distributed Control of Multiple-Bus Microgrid With Paralleled Distributed Generators

A microgrid is hard to control due to its reduced inertia and increased uncertainties. To overcome the challenges of microgrid control, advanced controllers need to be developed. In this paper, a distributed, two-level, communication-economic control scheme is presented for multiple-bus microgrids with each bus having multiple distributed generators (DGs) connected in parallel. The control objective of the upper level is to calculate the voltage references for one-bus subsystems. The objectives of the lower control level are to make the subsystems' bus voltages track the voltage references and to enhance load current sharing accuracy among the local DGs. Firstly, a distributed consensus-based power sharing algorithm is introduced to determine the power generations of the subsystems. Secondly, a discrete-time droop equation is used to adjust subsystem frequencies for voltage reference calculations. Finally, a Lyapunov-based decentralized control algorithm is designed for bus voltage regulation and proportional load current sharing. Extensive simulation studies with microgrid models of different levels of detail are performed to demonstrate the merits of the proposed control scheme.      

多源多变换微电网系统建模与动态稳定分析方法

微电网内部电力电子变换器接口型分布式电源广泛存在.电力电子接口微源具备与传统电力系统电源拓扑结构、控制方法和动态特性的差异性,控制方法的多样性、电力电子接口微源高渗透率将给低惯量微电网的协调控制和安全稳定运行带来严峻的挑战.多类型微源、多类型负荷在微电网内混合共存,可能引发源源耦合交互、负荷间交互以及源荷交互,不同特性的设备间相互作用将重新塑造区别与传统电力系统的动态响应特性,并诱发稳定性问题.本文首先归纳总结可再生能源渗透率不断提升下多源多变换微电网典型运行特性和存在的稳定性问题,详细阐述了含风光柴储多源多变换接口的微电网模块化动态建模方法.在此基础上,给出了用于微电网动态稳定分析的特征值分析方法和基本步骤.以珠海东澳岛多源多变换智能微电网为对象,基于提出的动态建模方法和动态稳定分析方法,给出建模与分析讨论结果.论文对多源多变换微电网系统动态稳定分析方法的论述可为微电网的广泛推广和应用提供建模和分析理论基础.     

微电网的电流均衡/电压恢复自适应动态规划策略研究

含多类型分布式电源的微电网已经成为了未来电力系统的重要发展方向,其中风能和光能在降低化石能源消耗和二氧化碳排放等方面有着极大优势,考虑二者之间强互补性的协同调度已被广泛研究.但风/光协同调度的微电网多关注分钟级的调度或优化问题而非风/光波动下秒级的实时电流按容量比例精准分担,简称电流均衡,而精准电流均衡有助于可再生能源的高比例消纳.因此,本文提出了基于自适应动态规划的微电网电流均衡和电压恢复控制策略.首先,构建包含风电整流型电能变换器和光电升压型电能变换器的广义风光拓扑同胚升压变换器模型,其提供了后续控制器设计的模型基础.其次,本文将电流均衡和电压恢复问题转化为最优控制问题,基于此,每个能源主体的目标函数转化为获取最优控制变量和最小电压/电流控制偏差,进而转化为求解哈密顿?雅克比?贝尔曼(Hamilton-Jacobi-Bellman,HJB)方程问题.基于此,提出了基于贝尔曼准则的分布式自适应动态规划控制策略以求取HJB方程的数值解,最终实现电流均衡和电压恢复.最后仿真结果验证了所提分布式自适应动态规划控制策略的有效性.     

微电网的电流均衡/电压恢复自适应动态规划策略研究

含多类型分布式电源的微电网已经成为了未来电力系统的重要发展方向,其中风能和光能在降低化石能源消耗和二氧化碳排放等方面有着极大优势,考虑二者之间强互补性的协同调度已被广泛研究.但风/光协同调度的微电网多关注分钟级的调度或优化问题而非风/光波动下秒级的实时电流按容量比例精准分担,简称电流均衡,而精准电流均衡有助于可再生能源的高比例消纳.因此,本文提出了基于自适应动态规划的微电网电流均衡和电压恢复控制策略.首先,构建包含风电整流型电能变换器和光电升压型电能变换器的广义风光拓扑同胚升压变换器模型,其提供了后续控制器设计的模型基础.其次,本文将电流均衡和电压恢复问题转化为最优控制问题,基于此,每个能源主体的目标函数转化为获取最优控制变量和最小电压/电流控制偏差,进而转化为求解哈密顿?雅克比?贝尔曼(Hamilton-Jacobi-Bellman,HJB)方程问题.基于此,提出了基于贝尔曼准则的分布式自适应动态规划控制策略以求取HJB方程的数值解,最终实现电流均衡和电压恢复.最后仿真结果验证了所提分布式自适应动态规划控制策略的有效性.     

State of the art in big data applications in microgrid: A review

The prospering Big data era is emerging in the power grid. Multiple world-wide studies are emphasizing the big data applications in the microgrid due to the huge amount of produced data. Big data analytics can impact the design and applications towards safer, better, more profitable, and effective power grid. This paper presents the recognition and challenges of the big data and the microgrid. The construction of big data analytics is introduced. The data sources, big data opportunities, and enhancement areas in the microgrid like stability improvement, asset management, renewable energy prediction, and decision-making support are summarized. Diverse case studies are presented including different planning, operation control, decision making, load forecasting, data attacks detection, and maintenance aspects of the microgrid. Finally, the open challenges of big data in the microgrid are discussed.     

Intelligent power management in micro grids with EV penetration

Large deployment of Electric Vehicles (EVs) adds new challenges in the operation of a microgrid. Assuming that a number of EV owners allow their batteries to charge when their cars are parked, this paper proposes an approach that aims to find suitable individual active power set-points corresponding to the hourly charging rate of each EV battery connected to the microgrid. A multi agent system based controller is designed to find these active power set points for optimal power management of EVs, distributed energy resources in the microgrid, and the loads.     

基于阻抗辨识的并联逆变器无功均分VSG控制

微电网作为"互联网+"智慧能源的重要补充,要求其在孤岛运行时,能够实现电压、频率有效支撑及负荷功率自动分配的功能。具有下垂特性的虚拟同步机(Virtual Synchronous Generator,VSG)控制由于具有惯性和阻尼环节,能够依据设计参数实现逆变器的功率自动分配,但由于微网内各逆变器连接线路阻抗的差异与变化,使得虚拟同步机控制无法仅依赖确定的下垂参数实现无功功率全工况均分。为此,提出一种改进的基于短时脉冲注入线路阻抗辨识的虚拟同步机无功-电压控制方法,通过预先辨识线路阻抗来补偿各逆变器产生的线路压降,实现了并联逆变器公共母线电压-无功下垂控制,以此来弥补线路阻抗压降对无功功率合理分配的影响。并对虚拟同步机控制下改进的无功控制原理及实现方案进行了分析,并通过仿真验证了方法的有效性。     

Load estimation for microgrid planning based on a self-organizing map methodology

This study presents a novel load estimation method for isolated communities that do not receive energy or only receive it for a limited time each day. These profiles have been used to determine the installed capacity of generating units for microgrid electrification projects. The social characteristics and lifestyles of isolated communities differ from those in urban areas; therefore, the load profiles of microgrids are sensitive to minor variations in generation and/or consumption. The proposed methodology for obtaining the residential profiles is based on clustering algorithms such as k-means, a self-organizing map (SOM) or others. In this work, SOM clustering is considered because it allows a better interpretation of results that can be contrasted with social aspects. The proposed methodology includes the following components. First, the inputs are processed based on surveys of residents that live in each socio-economic level of housing and the community. Second, family types are clustered using an SOM, from which relevant information is derived that distinguishes one family from another. Third, the load profiles of each cluster are selected from a database. Additionally, social aspects and relevant energy supply information from communities with similar characteristics are used to generate the required database. The SOM for the clustering of families of the community with available energy measurements is used as an initial guess for the clustering of the families in the community with unknown energy measurements.The methodology is applied and tested in the community of El Romeral, Chile, where a microgrid will be installed. The SOM technique compares favorably with a benchmark method that uses the average load profile of a community; furthermore, the SOM clustering algorithm for the methodology is favorably compared with the k-means algorithm because the results obtained by SOM are consistent with the social aspects.     

MAS Based Distributed Automatic Generation Control for Cyber-Physical Microgrid System

The microgrid is a typical cyber-physical microgrid system (CPMS). The physical unconventional distributed generators (DGs) are intermittent and inverter-interfaced which makes them very different to control. The cyber components, such as the embedded computer and communication network, are equipped with DGs, to process and transmit the necessary information for the controllers. In order to ensure system-wide observability, controllability and stabilization for the microgrid, the cyber and physical component need to be integrated. For the physical component of CPMS, the droop-control method is popular as it can be applied in both modes of operation to improve the grid transient performance. Traditional droop control methods have the drawback of the inherent trade-off between power sharing and voltage and frequency regulation. In this paper, the global information (such as the average voltage and the output active power of the microgrid and so on) are acquired distributedly based on multi-agent system (MAS). Based on the global information from cyber components of CPMS, automatic generation control (AGC) and automatic voltage control (AVC) are proposed to deal with the drawback of traditional droop control. Simulation studies in PSCAD demonstrate the effectiveness of the proposed control methods.      

微电网耦合碳捕集封存与需求响应的多时间尺度鲁棒优化调度

随着可再生能源的普及,微电网成为了一个可持续能源供应的重要工具。然而,由于其源荷多不确定性和碳排放挑战,优化调度成为了一个难题。本文提出了一种基于储液式碳捕集封存和需求响应的多时间尺度鲁棒优化调度方法,旨在降低微电网的碳排放和提高其运行性能。该方法在日前阶段以1小时时间间隔进行调度,采用鲁棒优化处理不确定性中的低频分量,同时结合碳捕集技术和需求响应以促进调度计划的低碳经济性。日内阶段以15分钟时间间隔进行调度,采用模型预测控制跟踪并滚动修正日前鲁棒经济调度计划,以应对源荷预测误差和不确定性中的高频分量,确保微电网实际功率平衡。仿真分析表明,这种方法可以显著降低微电网的总成本和碳排放量,提高了微电网的运行性能和可持续性。