计及风电场和储能系统联合运行的输电系统扩展规划

储能系统可以为电力系统提供调频和备用等辅助服务,与风电场联合运行时还可以平抑风电功率波动,改善系统运行的安全性和经济性.风电场和储能系统(简称风储系统)联合运行已成为一种受推崇的运行模式.因此,在未来的输电系统规划中就需要适当考虑风储系统的联合运行,这正是文中旨在解决的中心问题.首先,以钠硫储能系统为例,分析了储能系统成本与可放电次数的关系;在此基础上,提出了风储系统联合运行策略,在降低储能系统成本和平抑风电功率波动之间进行合理折中.之后,建立了考虑风储系统联合运行的输电系统两层规划模型,综合考虑了输电投资成本和风储运行成本,采用改进粒子群算法求解所建立的优化模型.最后,用18节点和46节点算例系统说明了所提出模型与方法的可行性和有效性.     

Congestion Management Considering Optimal Placement of Distributed Generator in Deregulated Power System Networks

This article presents an effective methodology for congestion management in deregulated power system networks considering optimal placement of a distributed generator. The novelty of this method is that the optimal placement of a distributed generator in a deregulated power system is decided on the basis of bus impedance matrix (Z_bus ) based contribution factors. The Z_bus -based contribution factors are independent of slack bus location, which complies with the prevailing competitive environment. The congestion management problem formulation comprises the maximization of social welfare function subject to power balance and transmission congestion constraints. The maximization of the social welfare function causes maximization of consumer benefits and minimization of supplier generation cost and distributed generators. The proposed methodology has been simulated on an IEEE 30-bus system, and comparisons of results are presented with and without distributed generators. The results show that the proposed approach gives significant improvement in social welfare and decreases congestion rent with distributed generator placement.     

基于改进人工蜂群算法的区域电网储能系统能量管理优化策略

为提高新能源消纳水平及系统运行效率,需对储能系统充放电功率进行优化,以平抑功率波动,降低网络损耗,提高经济效益。基于源荷状态判断储能各时段充放电状态,以区域日网损降低收益、日高储低放套利收益及日环境效益最大为目标,综合考虑储能自身约束及网架潮流状态约束等条件,建立了区域电网储能能量管理优化模型。求解过程中提出了一种改进人工蜂群算法（improved artificial bee colony,IABC）,并针对吐鲁番区域网架结构及运行特点进行了建模仿真。结果表明,对储能进行能量管理优化可提升整体经济效益,且改进人工蜂群算法具有很好的全局搜索能力及收敛性。     

基于TCSC技术和粒子群优化算法的电力系统阻塞疏导方法

提出了一种基于晶闸管控制的串连电容器(thyristor controlled series capacitor,TCSC)技术和粒子群优化算法的电力系统阻塞疏导方法。首先根据线路灵敏度分析确定安装TCSC的线路;然后提出了电力市场环境下电网中含有TCSC装置的阻塞疏导计算数学模型;最后运用粒子群优化算法对这一数学模型进行参数优化,达到疏导电网阻塞的目的。IEEE 14节点系统算例表明,基于TCSC技术进行电网阻塞疏导是有效、合理的。     

Optimal Thermal Unit Commitment Integrated with Renewable Energy Sources Using Advanced Particle Swarm Optimization

This paper presents a methodology for solving generation planning problem for thermal units integrated with wind and solar energy systems. The renewable energy sources are included in this model due to their low electricity cost and positive effect on environment. The generation planning problem also known by unit commitment problem is solved by a genetic algorithm operated improved binary particle swarm optimization (PSO) algorithm. Unlike trivial PSO, this algorithm runs the refinement process through the solutions within multiple populations. Some genetic algorithm operators such as crossover, elitism, and mutation are stochastically applied within the higher potential solutions to generate new solutions for next population. The PSO includes a new variable for updating velocity in accordance with population best along with conventional particle best and global best. The algorithm performs effectively in various sized thermal power system with equivalent solar and wind energy system and is able to produce high quality (minimized production cost) solutions. The solution model is also beneficial for reconstructed deregulated power system. The simulation results show the effectiveness of this algorithm by comparing the outcome with several established methods. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

计及需求响应的风电储能两阶段调度优化模型及求解算法

为解决风电功率不确定性对系统稳定运行带来的影响,在含风电的系统优化调度问题中引入需求响应和储能系统。首先利用区间法模拟风电场景并构建了基于Kantorovich距离的场景削减策略,然后分别在需求侧和发电侧引入需求响应和储能系统,结合2阶段优化理论,以风电日前预测功率和超短期预测功率作为随机变量及其实现,构建了计及需求响应的风电储能2阶段调度优化模型。为求解该模型,在传统二进制粒子群算法中引入混沌搜索,构建了混沌二进制粒子群算法。最后,以IEEE 36节点10机系统进行算例仿真。结果表明,混沌二进制粒子群算法能够得到全局最优解,适用于风电储能系统2阶段模型求解;利用需求响应和储能系统的协作效应,可以抑制风电功率的不确定性,提高系统风电利用效率,降低系统发电煤耗水平,因此综合效益显著。     

考虑风功率分布规律的风电场无功补偿容量优化决策

双馈型风电机组的无功调节范围随其有功功率输出变化而存在波动性,极端条件下,又有其不可调节性,由此必然降低其对自身电压水平支撑的持续性。为此,在依据功率估算数据对风电场输出功率分布特性进行统计分析的基础上,提出考虑风功率分布特性的风电场无功补偿容量优化决策方法。该方法在充分计及双馈感应发电机无功调节能力与风功率分布特性的前提下,以无功补偿的投资成本与运行成本最小化为目标,构建无功补偿容量优化计算模型。该研究可使双馈型风电场的无功补偿决策更具针对性,并以最小代价实现该类风电场连续、无缝的无功电压调节。应用改进粒子群优化算法对所构建算例系统进行求解,分析结果表明了该研究的有效性。     

计及风电汇集系统静态电压稳定性的网储联合规划

化石能源急剧消耗和生态环境问题的凸显,使得以风电、光伏为代表的可再生能源迅速发展。然而,网源发展不协调、风电随机性及并网点电压稳定性等一系列问题阻碍了风电大规模并网。综合考虑风电的不确定性、规划方案的经济性和风电场汇集系统静态电压稳定性等3个方面,建立了计及风电场接入的网储联合规划模型。考虑到模型的非线性特征,提出利用改进粒子群算法求解联合规划问题。改进的Garver-6节点系统算例结果表明,联合规划模型可以有效减少弃风率,在考虑经济性的同时兼顾稳定性问题。     

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.     

孤立微网的多目标能量管理

针对孤立微网系统的能量管理问题,利用蓄电池和超级电容的互补特性,以经济性和环保性为优化目标,提出了一种采用混合储能系统的微网多目标能量管理方法。求解方法分为2步,首先,综合考虑当前和未来可再生能源发电期望和负荷需求、不同调度时段、储能装置的实时荷电状态等因素,采用模糊控制对混合储能系统进行管理。其次,在计及多种约束条件下,利用改进的粒子群优化算法对微电源出力进行经济环保优化调度,得到微网系统的最终运行方式。该方法不仅能实现孤立微网经济、环保、可靠运行,还可延长蓄电池使用寿命,提高储能系统经济性,通过多组实验对比,验证了模型的有效性和可行性。     

风力发电系统中储能容量的优化配置

在风力发电系统中,合理地规划配置储能系统的容量对于风力发电产业的长远发展具有非常重要的意义。首先建立了电池储能系统的模型,提出一种基于该类储能系统的容量优化配置策略,并在此基础上将电池储能系统的全生命周期成本作为储能容量的优化目标,建立了以发电系统能量缺失率等运行指标为约束条件的储能容量优化模型,运用粒子群算法对该复杂优化配置模型进行求解计算。通过对算例系统的求解,验证了所建模型和算法的正确性和有效性,同时也为风力发电系统中储能单元的容量优化提供了参考。     

高比例可再生能源接入下考虑输配协同的输电网规划测试系统

以风光为主的高比例可再生能源通过不同电压等级接入电网,输电网与配电网之间的互济需求增强,促使输电网规划考虑与配电网协同,然而协同规划却面临着缺乏测试系统的难题.因此,构建了一个高比例可再生能源接入下考虑输配协同的输电网规划测试系统HRPTD-20,描述了系统的源网荷基本特性并给出了相关数据.系统包含2个区域、3个电压等级,以及3种传统和3种未来典型配电网结构场景,可再生能源装机容量占比接近55％.基于考虑输配协同的输电网规划模型求解,给出了测试系统的基准规划方案.对比分析验证了该测试系统可以用于输配电网协同规划,有助于提高可再生能源通过不同电压等级接入输配电网的消纳能力.     

基于改进场景聚类算法的海上风电储能优化配置研究

在平滑海上风电出力波动的应用需求下,提出一种储能优化配置方法。利用小波包分解算法对海上风电出力曲线进行分解,得到储能系统全年功率响应曲线。采用基于云模型和模糊C均值聚类算法相结合的改进场景聚类算法,对储能全年功率响应曲线进行聚合,生成储能功率响应典型场景。以储能年综合成本最低为目标,构建储能优化配置模型。采用粒子群算法对海上风电储能优化配置模型进行求解,最后通过算例仿真对所提方法和模型进行分析验证。结果表明：所提模型和方法能综合考虑海上风电场侧储能的实际运行特性,可有效指导海上风电场的储能配置和建设规划。     

楼宇能量管理系统的光伏消纳与储能调度研究

随着分布式能源渗透率的不断提高,构建以楼宇为单位的能量管理系统成为用户侧能源消费结构变革的必然趋势。文章以含有光伏和储能装置的楼宇为对象,根据负荷特性将楼宇负荷分为可调度负荷和不可调度负荷,建立楼宇电费最低和光伏最大本地消纳模型,研究这2个模型在不同权重系数下的目标函数,采用改进离散二进制粒子群优化算法(discrete binary particle swarm optimization algorithm,DBPSO)优化可调度负荷的工作时间。在不同情形下进行仿真,验证了所提模型在光伏本地消纳和楼宇能量管理方面的有效性和经济性。     

基于中枢解耦与演化博弈的多农业园区综合能源系统优化运行

农业园区用能需求集中且源荷多元化,当多个农业园区缺少合理运行方法且分布式接入农网,势必会对农业园区效益与农网安全产生不利影响.针对上述问题,提出一种基于中枢解耦与演化博弈的多农业园区综合能源系统(agricultural integrated energy system,AIES)优化运行方法.首先,构建含电气热的AI...     

Demand Side Management for Stand-Alone Microgrid Using Coordinated Control of Battery Energy Storage System and Hybrid Renewable Energy Sources

Abstract

Distributed generation with battery energy storage (BES) system is an alternative solution for stand-alone AC supply systems. This article addresses the issue of voltage variability of stand-alone microgrid with the adoption of intelligent micro-source controllers. The high energy density of the BES system is used to compensate the voltage fluctuation. A control mechanism is designed to regulate the flow of electrical energy from BES system. The control structure of DC–AC microgrid integrated with BES system is proposed, and the study is extended to explore the mechanism of demand side management (DSM) with the incorporation of voltage-droop characteristics. The BES system is interlinked to maintain the voltage to a desirable range and the proposed work is intended to show the introductory concept on DSM accomplished through voltage-reduction at the consumption-side. The proposed control strategy not only achieves frequency-regulation in adherence to IEEE Standard-1547, but also maintains customers’ quality of service while customizing voltage for regulating DSM. The basis of regulating the nickel-metal-hydride battery is state-of-charge of BES system and voltage generated by the hybrid sources consist of photovoltaic and wind energy systems. The adequacy of the proposed control scheme with BES-module is validated using time-domain simulation studies considering voltage-dependent-loads.     

基于自适应变异粒子群算法的双馈风电机组等值建模

风电机组等值建模是风电并网研究的基础,文中提出一种基于自适应变异粒子群(AMPSO)算法的双馈风电机组等值建模方法。首先依据风力机型号进行机群划分,通过简化双馈风电机组控制策略,建立了双馈风电机组等值模型。根据模型中各参数对双馈风电机组运行特性的影响特点,将其分为暂态参数和稳态参数,采用收敛速度快、通用性强的AMPSO算法对稳态参数寻优,采用试测法辨识暂态参数。通过仿真,验证了所述等值方法精确、简便,适用于大规模风电场接入电网的分析计算。     

Optimal Resource Management of Microgrids in a Competitive Market Environment Using Dual Layer Control Approach

Abstract

Renewable integrated microgrids effectively contribute in reducing GHG emissions substantially, at a global level. A multi-agent control system to facilitate information exchange for a microgrid participating in the deregulated framework of the electricity market is proposed. A novel energy management system, aimed at the effective utilization of RES and stored energy in PHESS in order to ensure maximum priority based social benefit to the microgrid controller is presented. The intermittent nature of RES which might cause uncertainties in availability during real-time dispatch, is effectively dealt in the proposed dual layer control approach, through optimal usage of PHESS and employment of load prioritization technique in the proposed renewable microgrid. Thus, the research carried out in this work not only ensures the committed power exchange is maintained constant in both DASL and RTDL, but also contributes significantly in PBSB maximization of the microgrid, while managing real-time uncertainties in RES availability. The importance of pre-prioritizing the loads is put forth, in order to ensure that, if load curtailing needs to be done during peak demand intervals, the critical loads must not suffer. The developed algorithm has been successfully implemented on a 16 bus microgrid interconnected with a 30 bus main grid.     

The Smart Grid—State-of-the-art and Future Trends

Abstract

This paper introduces Smart Grid and associated technical, environmental and socio-economic, and other non-tangible benefits to society, and articulates the need for the concept and the fact that it is a dynamic interactive, real-time infrastructure that responds to the challenges of designing and building the power system of the future, rather than being simply a marketing term. To illustrate the diversity of terminology, the paper compares an Electric Power Research Institute (EPRI) definition with that suggested by a study group of the International Electrotechnical Commission (IEC). Next, a paper sponsored by the Canadian Electricity Association (CEA) that cites three example definitions to highlight the diversity of views of Smart Grid is briefly reviewed. Early misconceptions and characterizations of Smart Grid are discussed as a prelude to addressing challenging issues that motivate developing and implementing related innovative technologies, products and services. The paper then discusses the potential promise of the Smart Grid, which is embedded in its often-cited attributes of efficiency, accommodating, quality focus, enabling and self-healing to name some. The paper then addresses some of the often-cited impediments to accepting Smart Grid which are based on concerns and issues confronting its forward progress, adoption and acceptance. Distribution Automation (DA) and embedded intelligence are discussed emphasising self-healing, optimizing operation and facilitating recreation and recovery from abnormal events. Functional and integration requirements of Distributed Energy Resources (DER,) are detailed. Smart Consumption Infrastructure elements of Distribution Management Systems (DMS,) Automated Metering Infrastructure (AMI,) Smart Homes (SH), and Smart Appliances (SA,) are discussed. Following the introductory section, this paper summarizes contributions included in the double issue 42(3–4) of the Electric Power Components and Systems Journal. To begin, papers are offered discussing smart grid activities in China, India, and the development of a Smart Grid roadmap for the US State of Kentucky. The approaches of each of these cases reflect the diversity of policy initiatives in these jurisdictions. Two state of the art reviews are given next. The first considers distribution network active management and future development trends in technologies and methods, where centralized and decentralized management frameworks and applying agent-based coordination are discussed. The second offers a review of smart home technologies and the goals of an energy management system (SHEMS). This section is concluded by a letter providing an overview of recent and expected advances nanotechnology applications in Smart Grid. Following the state of the art review section, ten papers offering new and innovative research approaches and results are included. These papers cover Smart Grid topics such as real-time energy control approach for smart home energy management systems, optimal operation of energy- efficient buildings with Combined Heat and Power (CHP) systems, energy management and control of Electric Vehicle (EV) charging stations, voltage-frequency control of a voltage source inverter (VSI) in a smart islanded microgrid, smart generation scheduling for wind-thermal-pumped storage systems, optimized power system restoration, robust data transmission upon compressive sensing, data fusion for wide-area oscillation monitoring, satellite based GPS synchronized monitoring systems, stability in Smart Grid with emerging renewable energy techno- logies.