A STATIC VAR COMPENSATOR CONTROL STRATEGY TO MAXIMIZE POWER SYSTEM DAMPING

ABSTRACT

A study of power system transient stability enhancement through effective use of static VAR compensator is presented. Optimal theoretical control strategy is derived from direct Lyapunov method for multi-machine system model as a control which forces the fastest dissipation of energy released in the system by a given fault. Strategy of this theoretical optimal control uses signal based on the state variables not available at the compensator bus. It is shown in the paper that optimal control signal can be replaced by the time derivative of locally measurable electric quantities. Dynamic properties of two such quantities are compared using eigenvalue analysis. On the basis of this study a current-orientated control strategy is proposed. Its validity and robustness is confirmed by simulation results for nonlinear multi-machine model.     

基于一致性算法的直流微电网储能系统功率分配技术

以直流微电网中的分布式储能系统为研究对象,分析了储能单元间存在的功能、参数和信息的不对称性,并提出了混合储能单元多工作模式下最大输出功率及等效荷电状态(SOC)的评估方法。在此基础上,提出了一种基于离散一致性算法的分布式储能系统负荷功率分配分层控制策略:在下层,以储能单元多模式参数评估为依据,使用动态下垂控制对各储能单元输出功率进行一次分配;在上层,以减小分布式储能系统等效SOC差异为目标,利用相邻单元之间的的弱通信,使用离散一致性算法产生电流修正量,直接调节下垂控制电流参考值,动态调整处于不对称工作状态的各混合储能单元的输出功率。最后,在MATLAB/Simulink中建立了仿真模型,对所提控制策略进行了仿真验证。     

MPC-Based Distributed Control for Intelligent Energy Management of AC Microgrids

Abstract

Power scheduling of distributed storage devices and renewable energy resources in microgrids is crucial for their reliable and optimal operation. Conventionally, the power scheduling problem is solved with a centralized energy management control, which has access to all the information from the overall system. This type of control has limitations related to computational burden, communication latencies, and missing information. Therefore, the primary objective of this paper is to propose a distributed energy management strategy, to reduce the operational cost of microgrids, and to enhance the computation time. The strategy is formulated based on the model predictive control method since it allows the control action in the current time-step to be optimized while taking into account the future time-steps. Outputs of the energy management system are the optimal power references generated for all energy resources based on the system’s demand, constraints, and objective functions. To illustrate the effectiveness of the distributed model predictive control, the proposed algorithm is validated through simulation, on a microgrid candidate via MATLAB/Simulink, and controller hardware-in-the-loop experiment on a LabVIEW platform using National Instruments control devices (myRIO). The results will show the computation time in the distributed IEM method is improved compared to centralized IEM. Additionally, the results will show the robustness of the distributed IEM in the plug and play scenario.     

钻井直流微电网冲击功率的混合储能平衡技术

受限于钻井直流微电网的功率动态调节缓慢,冲击性负载对钻井直流微电网电能质量造成严重影响。为了解决直流微电网冲击功率供需均衡问题,提出以混合储能环节为核心的补偿方法。基于超级电容和蓄电池储能的互补特性,在传统混合储能控制策略的基础上,提出一种纳入蓄电池参考电流约束和电流动态分配的混合储能控制策略。在保证蓄电池电流限制值不被超越的前提下,实现冲击功率在混合储能单元内的优化分配,从而既能够延长蓄电池生命周期,又显著缩短了混合储能系统冲击功率补偿的动态响应时间,解决了钻井直流微电网电压波动的问题。仿真与实验验证了所提控制策略的有效性与可行性。     

基于改进一致性算法的孤岛直流微电网储能系统分布式控制策略

针对孤岛直流微电网中多个储能单元的协调控制,提出了一种基于改进一致性算法的储能系统分布式控制策略。首先,提出了一种改进一致性算法,并证明该算法相比经典一致性算法收敛时间更短。然后,基于改进一致性算法,提出了一种储能系统分布式控制策略,通过合理调节各储能单元的功率,维持了直流母线电压的稳定、避免了储能电池的过充过放。该策略具有两种运行模式,分别适用于微电网有扰动与无扰动的情况,在及时调节储能单元功率的同时,减少了控制所需的通信量和计算量。最后,搭建了孤岛直流微电网系统仿真算例,仿真验证了所提控制策略的有效性及其在控制时间方面的优越性。     

基于可控超导储能的波动负载补偿

提出了一种应用可控超导储能(SMES)装置对波动负载进行补偿的方法。它可以使电网输入的有功功率保持恒定,同时对负载的无功功率进行补偿以提高功率因数。为了有效地进行功率控制,SMES装置的变流器采用电流源型的拓扑结构并与负载并联连接,同时采用闭环控制方法以提高系统的动态性能。为减小交流电流谐波成分,采用了优化脉宽调制开关策略。最后给出了一套20kJ／15kW SMES装置的仿真和实验结果。     

基于DMPC和储能单元约束的分组一致性控制策略

针对多个电池储能单元间的分布式协调控制问题,提出了一种分布式储能单元分组一致性控制策略。首先,提出了一种基于分布式模型预测控制和状态约束的加权一致性算法,能考虑各储能单元的功率约束条件并快速完成多储能单元的功率分配。其次,提出了一种储能单元分组控制策略。根据荷电状态(state of change, SOC)信息设定储能单元的权重,达到改善储能单元SOC的一致性的目的。同时基于所提一致性算法,制定储能单元组间协调控制策略和储能单元效率提升策略,从而达到改善储能系统调节能力、延长储能系统寿命和提升能量转换效率的目的。最后,在Matlab中构建含8个电池储能单元的微电网系统,对所提算法和控制策略进行仿真分析。仿真结果表明,所提算法和控制策略在提升收敛速度、优化控制效果、延长储能系统寿命以及提升储能系统运行效率方面均具有一定优势。     

基于一致性算法的储能单元分布式优化调度策略

随着新型储能技术的快速发展和产业化,集成海量储能单元,平抑可再生资源发电所引起的电网功率波动成为可能.但是大量储能单元分布分散,加之电池储能单元中内阻的存在会影响其存储效率,传统集中式调度难度很大.因此设计了一种基于多智能体一致性的考虑电池内阻的储能单元完全分布式优化调度策略.通过利用该完全分布式算法对储能单元进行实时...     

基于分布式光伏电站和储能系统的家庭能效管理策略研究

设计了一种基于Zig Bee技术的智能家居系统,该系统由家庭网络、家庭服务器和手机终端构成,简单有效且可扩展性良好。研究了基于分布式光伏电站和储能系统的家庭能效管理系统,通过建立家庭用电总费用最低的目标函数,结合分时电价给出了家庭能效管理的控制策略。通过决策储能电池的充放电功率以及可平移负荷的工作时间,实现对家庭负荷的优化利用,提高运行经济性,达到经济效益最大化,也能起到削峰填谷的作用。最后,结合某家庭用户家用电器设备进行仿真测试,结果表明该能效管理算法能有效节省家庭用电总费用,证明算法的有效性和可行性。     

列车再生制动能量管理及控制系统研究

为回收利用交流电气化铁路列车产生的再生制动能量,研究了再生制动能量管理及控制系统.提出一种基于牵引负荷状态的能量综合管理策略,以牵引变压器两供电臂负荷功率为信息载体表示系统的不同工作模式,多种工作模式可相互切换.在混合储能装置内部功率分配中,通过引入锂电池荷电状态SOC(state of charge)及超级电容中间调...     

Firefly Optimization Algorithm for the Reactive Power Control of an Isolated Wind-Diesel System

This paper investigates the application of firefly optimization algorithm to design an optimal control for voltage stability of a stand-alone hybrid renewable generation unit based on reactive power control. The studied renewable generation unit mainly consists of a permanent magnet induction generator driven by wind turbine and a synchronous generator driven by diesel engine. A STATCOM is used to stabilize the terminal load bus voltage via compensating of reactive power. The main control objective aims to stabilize the terminal load voltage against any disturbances in load reactive power and/or input wind power by adjusting the total system reactive power. This is accomplished by controlling STATCOM phase angle and hence to control the load bus voltage and also by controlling the excitation voltage of the synchronous generator. The proposed renewable energy power system based on the proposed optimal controller has been tested through step change in input wind power and load reactive power. The system performance based on the proposed control is compared with model predictive control, a robust H_∞ control, and a classical PI control.     

Application of Hopfield Neural Network for Harmonic Current Estimation and Shunt Compensation

Harmonics generated by nonlinear loads pollute the power system and affect the operation of equipment connected to it. Hence, harmonic mitigation is of prime concern to a power system engineer. Artificial Neural Network (ANN) is a nonlinear signal processing technique, which is built from interconnected elementary processors called neurons. In this article, a Hopfield Neural Network (HNN) based control algorithm for shunt compensator in a power distribution system is realized. The Hopfield network is modeled using energy minimization principle and consists of “n” interconnected neurons. The HNN is used to estimate different harmonic components present in distribution system operating with nonlinear loads. It also provides suitable control signals to the shunt compensator for compensation of various power quality issues such as power factor correction, load balancing, and harmonic reduction in the distribution system. Detailed experimental results are presented along with simulation studies on the prototype model developed in the laboratory and these results demonstrate the feasibility of the proposed method of control in DSTATCOM. The comparison of the HNN-based compensation technique with a popular and effective control algorithm based on Least Means Square (LMS) is also presented in this article.     

A Joint Smart Generation Scheduling Approach for Wind Thermal Pumped Storage Systems

Abstract

Energy storage plays a crucial role in the development of smart grids with high wind power penetration. Pumped storage is an effective solution for smoothing wind power fluctuation and reducing the operating cost for a wind thermal power system. The joint generation scheduling of power systems with mixed wind power, pumped storage, and thermal power is a challenging problem. This article proposes a novel two-stage generation scheduling approach for this problem in the contexts of smart grids. Through optimization, a day-ahead thermal unit commitment and pumped storage schedule are provided; then, in real time, the pumped storage schedule is updated to mitigate the wind power forecasting error and hence avoid the curtailment of wind power generation. The proposed model aims to reduce the total operating cost, accommodate uncertain wind power as much as possible, and smooth the output fluctuation faced by thermal units, while making the system operate in a relatively reliable way. A binary particle swarm optimization algorithm for solving the proposed model and the pumped storage schedule update algorithm are also presented. The model and algorithm are tested on a ten-generator test system.     

基于串级PI-(1+PD)算法的含飞轮储能互联电网AGC控制器设计

针对传统控制器存在的响应速度较慢、超调较大及鲁棒性较差等问题,提出一种基于串级PI-(1+PD)算法的含飞轮储能互联电网AGC控制器设计方法。首先,建立含飞轮储能的两区域互联电网AGC系统模型,模拟飞轮储能联合火电机组参与AGC的过程。然后,设计一种基于串级PI-(1+PD)算法的AGC控制器。外环采用PI控制,内环采用带滤波系数的(1+PD)控制。在保证系统稳态性能的前提下,提高动态响应速度和抗扰能力,并通过粒子群算法的迭代寻优获得最优的控制器参数。最后,基于Matlab/Simulink进行算例仿真分析。结果表明：与传统PID控制和PI-PD控制相比,所提方法不仅具有更快的响应速度与更小的超调量,而且增强了系统抵御内部参数摄动的鲁棒性。     

Identification and Optimal Control of a Generating Unit in an Interconnected System

Abstract

The paper describes the identification and discrete-time optimal control design of a generating unit, operating in a multimachine environment. The system comprises three generating units which are interconnected to supply a common load area in addition to their local loads. The generating unit, which is the most effective candidate for optimal control application, is selected first on the basis of eigenvalue analysis. A low-order state-space model of this unit is obtained by using recursive least-squares identification. On the basis of this model, a local state-feedback optimal controller is designed by using the dynamic programming procedure. The controller provides integrated control to the generator exciter and turbine-valving, and is implemented on a fairly detailed non-linear model which simulates the interconnected system. A comparison is made between this optimal controller and conventional controllers which are in common use in power stations. The simulation results, which include 3-phase fault tests and stability boundaries, show the appreciable improvement in system performance obtained by adopting the optimal controller based on the identified model.     

基于储能实时修正双环控制的微电网能量管理方法

提升各类可再生能源(RES)高效并网发电是应对全球环境恶化、能源短缺的有效方法。但各类能源的出力特性不同,如何有效分配使用各类能源是目前微电网需要解决的主要问题。针对此问题,采用冷热电联产(CCHP)与储能系统相结合的运行方式,利用优化算法分层控制微型燃气轮机出力和储能系统的充放电功率,综合协调光伏、风电、微型燃气轮机输出和用户负荷需求,解决能源出力和负荷需求分布不对等问题,实现RES高效使用。其中,针对储能系统充放电控制,提出一种基于预测数据实时修正的能量管理方法,可以减小预测误差对储能系统充放电控制的干扰,提高能量管控的灵活性。实测数据试验结果显示,与固定阈值法和自适应算法相比,所提算法在充分利用各类能源基础上有效起到了削减负荷峰值的作用,减小冲击负荷对电网的影响,实现了能源的经济、优化利用。     

分布式光伏储能系统的优化配置方法

光伏发电的随机性和间歇性导致资源利用率低,储能具备控制灵活、响应快速的特性,是当前解决光伏并网和提高消纳的有效手段之一。目前,高昂投资成本是制约储能推广应用的关键,文中从成本角度出发研究了分布式光伏系统中储能的优化配置方法。首先,以分布式储能系统的投资和运行成本为目标,同时考虑储能接入位置、配置容量、荷电状态和电网运行状态等为约束条件建立双层优化模型;然后,介绍优化模型求解方法,外层采用遗传算法优化储能配置位置、功率和容量,内层采用粒子群算法结合MATPOWER潮流计算工具优化储能日内运行策略;最后,在Matlab软件中采用IEEE9节点系统验证了优化配置方法的可行性和有效性。     

Experimental Verification of Photovoltaic-based Three-phase Four-wire Series Hybrid Active Power Filter

Abstract—This article proposes a photovoltaic and battery interfaced series hybrid active power filter, which is comprised of a series active power filter and an inductor-capacitor (LC) shunt passive filter. The main benefits of the proposed system provide the compensation against the voltage harmonics, current harmonics, and voltage interruption for the whole day. A series active power filter demands a source of energy for compensating the voltage sag/swell. The proposed topology utilizes the green energy source with an energy storage unit to meet the DC-link voltage requirement of the series active power filter. The control strategy is based on the dual formulation of the compensation system principles with adaptive fuzzy logic controller. The simulation and experimental studies are carried out to validate the effectiveness of the proposed photovoltaic interfaced three-phase four-wire series hybrid active power filter.     

Particle Swarm Optimization-based Superconducting Magnetic Energy Storage for Low-voltage Ride-through Capability Enhancement in Wind Energy Conversion System

Abstract— This article presents a novel application of the particle swarm optimization technique to optimally design all the proportional–integral controllers required to control both the real and reactive powers of the superconducting magnetic energy storage unit for enhancing the low-voltage ride-through capability of a grid-connected wind farm. The control strategy of the superconducting magnetic energy storage system is based on a sinusoidal pulse-width modulation voltage source converter and proportional–integral-controlled DC-DC converter. Control of the voltage source converter depends on the cascaded proportional–integral control scheme. All proportional–integral controllers in the superconducting magnetic energy storage system are optimally designed by the particle swarm optimization technique. The statistical response surface methodology is used to build the mathematical model of the voltage responses at the point of common coupling in terms of the proportional–integral controller parameters. The effectiveness of the proportional–integral-controlled superconducting magnetic energy storage optimized by the proposed particle swarm optimization technique is then compared to that optimized by a genetic algorithm technique, taking into consideration symmetrical and unsymmetrical fault conditions. A two-mass drive train model is used for the wind turbine generator system because of its large influence on the fault analyses. The systemic design approach is demonstrated in determining the controller parameters of the superconducting magnetic energy storage unit, and its effectiveness is validated in augmenting the low-voltage ride-through of a grid-connected wind farm.     

综合环境保护及峰谷电价的水火电短期优化调度

为了使电力市场环境中的发电侧能够实现节能环保且高收益的发电目标,对机组出力变化与分时电价波动之间的关系进行了研究,构建了一种新的水火电短期优化调度模型,该模型以实现电力市场条件下最大发电收益为目标,同时综合考虑了峰谷分时电价和环境保护成本对发电侧经济效益的影响,还考虑了梯级水电站群的蓄水量、下泄流量、机组出力等约束条件,由此得出机组的优化调度方案。针对传统优化算法难以处理高维梯级水电站优化调度多约束条件的缺陷,利用微分进化算法对此优化模型进行求解,仿真计算结果证明了该模型的合理性和算法的有效性。