计及多智能体调度的冷热电联供型微网并网优化运行研究

基于多智能体技术以实现微网内各个代理之间的通信与协调控制为前提,建立了以发电成本和环境费用最小、并网收益最高为目标的冷热电联供型微网并网优化调度模型。该模型应用多代理混沌粒子群算法获得最优解和各微源最佳出力方案。在算例分析中选取某微网为参考系统,分别设置了3个典型场景,分析了在并网条件下微网加入冷热电联供系统后对优化目标结果的影响。采用多代理混沌粒子群算法,求得3个典型场景下的最优解。通过对比分析,验证了所提方法的可靠性。     

基于典型场景集的智能园区多能源微网多目标配置优化研究

构建面向智能园区的多能源微网是实现不同类型物理能源系统耦合,提高可再生能源终端能源消费占比的重要途经。通过制定恰当的多能源微网规划,对于保证多能源微网项目投资收益以及推动多能源微网有序发展具有重要意义。鉴于此,该文以园区级多能源微网为研究对象,在利用隐马尔可夫构建典型场景集以压缩系统历史数据的基础上,构建多能源微网配置多目标优化模型架构,并利用某园区实际负荷数据和分布情况,对比分析不同情景下2种不同配置方案的结果和优劣势,定量阐述了多能源微网在降低系统排放强度和系统投资成本方面的优势。     

配电台区灵活资源多时间尺度优化调度方法——以含虚拟储能的建筑微网为例

随着“双碳”目标的提出,配电台区接入大量分布式光伏、电动汽车、微网以及智能建筑等新元素,在给中低压配网安全运行带来挑战的同时也蕴含着巨大的灵活调节潜力。本工作以配电台区的智能建筑为例,提出一种含虚拟储能系统的建筑微网多时间尺度优化调度方法。首先对建筑围护结构的动态特性进行建模,构建了对建筑灵活性进行定量描述的虚拟储能模型,通过对虚拟储能的充放电过程进行优化调度来实现对建筑灵活性的定量利用;随后,在配电台区灵活资源多时间尺度优化调度模型中详细考虑了建筑的可调虚拟储能特性,分别在日前调度和日间修正两个时间尺度对建筑虚拟储能进行充分利用,提出以运行成本最低为目标的日前经济调度方法和跟踪微网联络线设定值的日间修正方法。最后以夏季制冷场景为例,利用典型建筑微网系统验证了本文所提多时间尺度优化调度方法的有效性。结果表明,本文所提方法可有效调度建筑的虚拟储能能力,实现在日前运行阶段降低建筑微网的运行成本,在日间运行阶段有效平抑可再生能源的随机波动。     

负荷不对称情况下的微网逆变器电流均分控制

由于低压微电网中负荷多以单相的形式出现,易出现三相负荷不对称而造成三相电压的不平衡,从而影响微网中的逆变器控制。在微网系统中三相负荷不平衡的情况下,对微网逆变器的控制和不平衡电流的均分控制进行研究。首先分析微网中在负荷不对称情况下的锁相和负序分量的提取方法,然后设计相对应的微网逆变器电压电流环,分析负序电流的均分条件,并提出一种基于微网中央控制器(MGCC)进行负序电流分配的逆变器负序电流均分控制方法,通过仿真和实验验证所提方法的有效性。     

抽蓄联合全可再生能源孤岛微网配置优化模型

针对全可再生能源孤岛微电网中的源荷功率平衡问题,研究基于抽水蓄能快速启停和强调节能力的风-光-抽蓄联合孤岛微电网电源与储能系统容量配置多场景鲁棒优化方法。针对海岛负荷需求特性及可再生能源、抽蓄资源分布特性,研究了适用于海岛的包含风、光、抽蓄和蓄电池组的孤岛微网拓扑结构模型;考虑海岛抽水蓄能及蓄电池电能储放特性、可再生能源出力不确定性以及岛内负荷需求不确定性,研究了微网功率协调优化方法,建立以孤岛微网成本最小为目标的容量配置鲁棒优化模型及其求解算法。以某海岛的实际负荷需求及可再生能源发电资源数据为基础的仿真结果表明,文章所提出的抽蓄联合孤岛微电网的容量优化配置方法,在实现经济性目标的同时,可以有效地提升可再生能源利用率和微网在多种场景下的运行可靠性。     

基于RDA的微网内部分布式电源均衡调度策略研究

针对微网内部分布式电源的调度问题,提出一种新能源电力系统环境下微网内部优化调度模型与解法。基于微网的多代理结构形式,建立微网内部分布式电源的均衡调度模型,利用模仿者动态算法处理微网内部调度资源分配问题。通过分析分布式电源的适应度系数与可调容量,提出多方共赢的微网内部分布式电源均衡调度策略。通过算例分析,与传统基于市场机制的微网调度方法相比,模仿者动态学习算法在解决微网内部分布式电源调度方面具有简便、易拓展的特点,同时又能有效控制调度成本,证明所提模型和算法的合理性和有效性。     

基于Hotbooting Q算法的多微网能量交易博弈模型

文章构建了多个风-光-储微网能量交易博弈模型,每个微网可根据风光等新能源机组的发电水平以及负荷需求,确定自己与其余微网和上级电网的能量交易策略。在对多微网能量交易博弈模型的求解上,提出了一种将热启动Hotbooting技术与Q-学习相结合的Hotbooting Q交易算法,并以电力市场相似场景下的大规模实验数据作为训练数据。通过算例表明,微网间能量交易减少了从上级电网的购电量,采用Hotbooting Q交易算法可加快系统的收敛速度,提高微网的交易学习效果。     

Overview on multi-microgrid technologies

随着分布式风电、光伏间歇性电源在中低压配电网中渗透率的提高,多个微电网可能共存于一个区域配电网,包含各微网间能量互济与协调控制的微网群技术开始引起广泛的关注。文中基于微电网技术及多微电网技术在相关领域的研究基础对微网群从典型特征及拓扑结构、数学描述、微网群能量管理及优化调度方法、微网群运行控制结构及对外控制方式、微网群多组态运行模式及对内控制策略等关键问题及研究现状进行了分析阐述。最后结合我国未来智能电网的建设规划,对微网群技术应用前景进行了展望。     

计及微网储能系统多尺度不确定性容量协调优化

微网系统中的分布式电源和负荷需求的随机问题促使微网储能容量决策成为一个研究的热点话题.本文提出了多时间尺度下微网系统中源-荷的随机性和预测出力偏差的不确定性的储能容量优化方法.利用该方法建立了系统能量平衡关系和鲁棒性经济协调指标,刻画出了储能系统容量优化方法和微网随机因子间的定量关系,并兼顾微网运行经济性的目标.结合分层理论建立了含分布式电源的微网储能容量的双层优化模型,并采用多目标粒子群算法对本文的优化模型进行求解.仿真结果表明,所提方法能够保证储能系统容量优化配置,同时能获得良好的经济效益.     

考虑激励型需求响应的孤立型微网优化运行

在微网中引入激励型需求响应,能够引导用户参与负荷削减,降低负荷峰值.在此基础上,以最小化微网运行成本为目标建立了孤立型微网优化运行模型.为便于编程求解,将模型中的非线性部分进行线性化处理.最后以一个包含风电、光伏发电以及电池储能系统的孤立型微网系统作为算例进行分析,计算结果验证了所建立模型的有效性.     

A cost-effective approach for optimal energy management of a hybrid CCHP microgrid with different hydrogen production considering load growth analysis

An optimum design and energy management of various distributed energy resources is investigated in a hybrid microgrid system with the examination of electrical, heating, and cooling demand. This paper suggested an optimal approach to design and operate a microgrid incorporating with battery energy storage, thermal energy storage, photovoltaic arrays, fuel cell, and boiler with minimization of the total operational cost of the hybrid microgrid. Two different hydrogen production methods are considered to assure the advantage of the developed proposed methodology. Furthermore, besides natural gas, residential and municipal wastes are collected and are utilized to produce electricity in fuel cell units. Load growth for different type of loads is also considered. The new number of households are added to the proposed system in different years and the proposed program is determined the optimum size of each employed resources to add each year for satisfying the total demand. To find out the optimum energy management and the optimum capacity of each employed distributed energy resources, a meta-heuristic Particle Swarm Optimization Algorithm is utilized. It is concluded from the results that by utilizing residential waste, the amount of natural gas consumption by fuel cells is reduced about 6.2%, and by utilizing residential plus municipal waste, the reduction is about 26.7%. It is also observed that the amount of CO₂ emission is reduced significantly (46.8%) in the case of utilization of produced heat by fuel cells. Finally, the results confirmed the efficacy of the suggested optimal energy management of the hybrid microgrid.     

Microgrid Dynamic Performance Improvement Using a Doubly Fed Induction Wind Generator

This paper describes a control approach applied on a doubly fed induction generator (DFIG) to provide both voltage and frequency regulation capabilities, and hence, an improvement in the dynamic behavior of a microgrid system. The microgrid system is assumed to be a portion of a medium voltage distribution feeder and is supplied by two distributed generation (DG) units, i.e., a gas-turbine synchronous generator and a variable-speed wind turbine with DFIG. A control approach algorithm is proposed for the DFIG unit to improve both voltage and primary frequency controls. Two distinct operation modes, i.e., grid-connected and islanding mode, are used in the proposed approach for proper transfer from normal to islanding operation. Case studies are simulated based on both planned and unplanned islanding scenarios to evaluate the performance of the control approach. The study results show that the proposed control approach for DGs in the microgrid increase the microgrid system's dynamic performance, reduce frequency changes, and improve bus voltages regulation during islanding and autonomous operations.      

基于改进灰狼算法的独立微电网容量优化配置

为降低独立微电网的综合发电成本,提高供电可靠性,研究基于改进灰狼优化算法的独立微电网电源容量优化配置方法。针对基本灰狼算法在进化后期由于种群多样性的缺失而易出现局部收敛或算法早熟的问题,提出一种具有全局寻优性能的改进灰狼优化算法。改进算法首先利用Tent混沌序列产生初始种群,以增强种群的多样性;其次,通过对收敛因子设置非线性调整策略来调节算法的全局探索与局部开发之间的平衡,提高算法的收敛性;最后引入柯西变异算子有效降低算法出现早熟收敛的概率,从而全面提高算法的优化性能。在分析风力发电机-光伏组件-蓄电池/柴油发电机独立微电网各发电单元特性的基础上,建立以年均系统成本最小化为目标的独立微电网容量优化配置模型,并利用提出的改进灰狼优化算法对该模型进行优化求解,仿真结果证明该算法具有一定的实用性和鲁棒性。     

基于有色Petri网的微电网被动式孤网控制方法研究

针对微电网发生被动式孤网事件后,进行并网模式转换为孤网模式的过程中的网架重构控制方法研究。利用有色Pe试网的方法,结合Multi—Agent微源协作模型构建微电网模式控制器,弥补了运用传统方法下微电网架重构时间长的缺点。在Muhi—Agent有色Petri网的基础上引入Petri网元件状态事件表,从而大大提高了重构的效率和加强了重构过程中的监控。利用IEEE16结合微电网的改进模型进行仿真验证,验证了基于Multi—Agent和有色Petri网模型的微电网模式转换控制方法,完全适用于微电网的被动式孤网转换中的经济型重构过程。     

Advanced optimal planning for microgrid technologies including hydrogen and mobility at a real microgrid testbed

This paper investigates the optimal planning of microgrids including the hydrogen energy system through mixed-integer linear programming model. A real case study is analyzed by extending the only microgrid lab facility in Austria. The case study considers the hydrogen production via electrolysis, seasonal storage and fueling station for meeting the hydrogen fuel demand of fuel cell vehicles, busses and trucks. The optimization is performed relative to two different reference cases which satisfy the mobility demand by diesel fuel and utility electricity based hydrogen fuel production respectively. The key results indicate that the low emission hydrogen mobility framework is achieved by high share of renewable energy sources and seasonal hydrogen storage in the microgrid. The investment optimization scenarios provide at least 66% and at most 99% carbon emission savings at increased costs of 30% and 100% respectively relative to the costs of the diesel reference case (current situation).     

孤岛运行模式下的微网经济负荷分配

研究了微网孤岛运行模式下的经济负荷分配问题,即在满足系统约束条件下如何优化微网中各微电源的出力,使微网的目标成本最小.建立了微网孤岛运行模式的经济负荷分配数学模型,然后采用改进粒子群优化算法对微网的经济负荷分配进行了研究.最后通过算例验证了数学模型与优化算法的正确性与自效性.     

基于二自由度内模控制的STATCOM对孤岛微电网电压稳定性的影响

鉴于含静止同步补偿器(STATCOM)的微电网孤岛运行时电压的稳定性是微电网未来发展面临的主要问题之一,针对孤岛微电网中的STATCOM设计了一种基于二自由度内模控制的控制器代替传统PI控制器,通过参数整定同时提高STATCOM的跟踪性能和鲁棒性,从而稳定孤岛微电网电压,通过建立阻抗模型,利用阻抗法分析两种控制方法的STATCOM对孤岛微电网电压稳定性的影响,并进行仿真验证。结果表明,与PI控制相比,基于二自由度内模控制的STATCOM可有效提升孤岛微电网电压的稳定性。     

A hybrid method for simultaneous optimization of DG capacity and operational strategy in microgrids considering uncertainty in electricity price forecasting

Recently, microgrids have attracted considerable attention as a high-quality and reliable source of electricity. In this work energy management in microgrids is addressed in light of economic and environmental restrictions through (a) development of an operational strategy for energy management in microgrids and (b) determination of type and capacity of distributed generation (DG) sources as well as the capacity of storage devices (SD) based on optimization. Net present value is used as an economic indicator for justification of investment in microgrids. The proposed NPV-based objective function accounts for the expenses including the initial investment costs, operational strategy costs, purchase of electricity from the utility, maintenance and operational costs, as well as revenues including those associated with reduction in non-delivered energy, the credit for reduction in levels of environmental pollution, and sales of electricity back to the utility. The optimal solution maximizing the objective function is obtained using a hybrid optimization method which combines the quadratic programming (QP) and the particle swarm optimization (PSO) algorithms to determine the optimum capacity of the sources as well as the appropriate operational strategy for the microgrid. The fuzzy set theory is employed to account for the uncertainties associated with electrical power price. Application of the proposed method under different operational scenarios serves to demonstrate the efficiency of the proposed scheme.     

A rank-based multiple-choice secretary algorithm for minimising microgrid operating cost under uncertainties

The increasing use of distributed energy resources changes the way to manage the electricity system. Unlike the traditional centralized powered utility, many homes and businesses with local electricity generators have established their own microgrids, which increases the use of renewable energy while introducing a new challenge to the management of the microgrid system from the mismatch and unknown of renewable energy generations, load demands, and dynamic electricity prices. To address this challenge, a rank-based multiple-choice secretary algorithm (RMSA) was proposed for microgrid management, to reduce the microgrid operating cost. Rather than relying on the complete information of future dynamic variables or accurate predictive approaches, a lightweight solution was used to make real-time decisions under uncertainties. The RMSA enables a microgrid to reduce the operating cost by determining the best electricity purchase timing for each task under dynamic pricing. Extensive experiments were conducted on real-world data sets to prove the efficacy of our solution in complex and divergent real-world scenarios.     

考虑下垂控制策略的孤岛微网最优潮流

考虑到孤岛微网与传统大电网最优潮流的不同,提出一种基于下垂控制策略的孤岛微网最优潮流,构建了下垂控制分布式电源的孤岛微网模型,并建立了以系统发电成本最小和负荷能力最大为优化目标的孤岛微网最优潮流,同时利用模糊数学方法对多目标函数进行了处理,并以改进的33节点微网为例进行仿真模拟,利用最优潮流的经典计算方法——内点法对模型进行求解。结果表明,所提优化方法在求解孤岛微网最优潮流方面具有有效性和实用性。