考虑源荷不确定性的电热系统联合调度

针对风电和电热负荷不确定的问题,提出计及源荷不确定性的旋转备用容量的优化方法,建立考虑电热备用耦合影响的调度模型。在日前阶段,以能源与负荷的预测量制定机组的出力方案,风电由于其预测精度较低,利用Beta概率密度函数来拟合风电出力,从而确定风电的不确定性带来的旋转备用容量,利用机会约束规划来处理不确定问题;负荷有较高的预测精度,但其波动性较强,使用概率场景生成和削减的方法制定不同场景下负荷备用需求的调整量,以日运行成本最低建立目标函数,运用改进的粒子群算法在IEEE30节点系统上验证了所提模型的经济性和鲁棒性。     

含高比例风光接入的输电网氢-电混合储能系统配置方法

目前高比例风光接入的输电网存在弃风弃光率普遍较高的问题,配置储能设备作为一种具有潜在效用的手段在当前的研究中备受青睐。针对输电网储能配置问题,为达到综合成本减小和弃风弃光率降低的目的,提出了一种基于双层规划模型的输电网氢-电混合储能系统配置方法。首先,对氢-电混合储能系统的特性进行建模,在此基础上建立输电网氢-电混合储能系统功率与容量配置双层规划模型。上层模型以配置储能后年综合成本最小为目标;下层模型以弃风弃光率最小为目标。其后基于蓄电池和氢储能的不同特性,提出一种氢-电混合储能系统配合策略。最后,针对该模型非线性多目标的特点,采用双层迭代粒子群算法与潮流计算相结合进行模型求解。在算例仿真部分,以某含有高比例风光接入地区输电网为例,进行氢-电混合储能系统容量和选址的优化配置,验证了所建模型和所提求解算法的可行性和有效性。提高了风电光伏出力在时序上的转移能力,减少了高碳化石能源的消耗量,降低了弃风弃光率。     

LCL型多逆变器并网系统谐振研究综述

多逆变器并网系统谐振将威胁电网的稳定运行。由于具备较好滤波效果,LCL型滤波器常被作为并网逆变器的输出滤波器。鉴于近年来又有很多LCL型多逆变器并网系统谐振机理分析和抑制方法被提出,有必要进一步梳理和总结。首先,介绍了多逆变器并网发电系统电路拓扑及等效电路。然后,梳理了频域分析法、模态分析法和多输入多输出模型分析法的基本概念及其在系统谐振机理分析中取得的新成果。接着,重点介绍了有源阻尼法、阻抗重塑法和采用有源阻尼器在系统谐振抑制方面的优点和局限性,揭示了虚拟电阻和陷波器的应用是上述方法的关键技术。同时,还分析了分层控制、控制器参数优化以及系统配置优化在系统谐振抑制方面的应用。最后,从新型电力系统建设趋势的角度,认为多逆变器并网系统正朝着大规模、多参数和不同控制方法并用的复杂系统方向发展,需要多种分析方法相结合才能准确分析其谐振机理。对于多逆变器并网谐振的抑制方法而言,传统有源阻尼法、阻抗重塑法和有源阻尼器将得到进一步深入研究,谐振在线监测技术或将成为该研究新的突破口。     

直购电背景下利用储能配合旋转备用的风电并网容量优化

为了解决大用户直购电与风电消纳之间的矛盾,文章提出利用大用户以直购电的形式消纳风电。首先,以上下旋转备用来应对风电的不确定性,引入储能系统弥补较高备用需求导致的上下旋转备用空间的不足。其次,在此基础上以大用户直购电背景下风电最优并网容量为目标建立了以发电企业为领导者,风电场为跟随者的主从博弈模型。最后,运用改进粒子群算法在含风电场的IEEE 30节点系统上进行仿真分析,算例验证了所提方法的有效性。     

考虑储能-智能软开关的主动配电网混合时间尺度鲁棒优化

随着配电网中光伏(photovoltaic, PV)发电渗透率增大,由于源/荷分布不均衡引起的馈线功率急剧波动、电压越限问题时有发生。提出一种考虑储能-智能软开关(soft open point, SOP)、光伏逆变器及无功电压控制 (var/voltage control, VVC)设备的主动配电网混合时间尺度鲁棒优化方法,以提高配网运行的经济性和稳定性。首先,通过在配电网线路末端间引入储能元件与SOP相结合(E-SOP)的柔性互联装置,建立计及多种调压设备的配电网日前-日内鲁棒优化模型;其次,建立基于鲁棒条件下的时序电压灵敏度逆变器下垂控制模型,以应对PV发电随机性强所带来的风险,达到维持系统电压稳定性、平衡配电网源/荷分布的目的;最后,以改进的IEEE 33节点系统为例进行仿真算例分析,验证了所提方法的有效性。     

考虑调度决策非预期性的多阶段随机规划调度策略

随着可再生能源渗透率的提高,分布式可再生能源带来的波动性、间歇性会传递至主网中,对系统安全运行造成影响,研究不确定性优化方法对系统实际运行具有一定的指导作用。传统的随机优化以及鲁棒优化方法不满足系统实际运行的非预期性要求。文章以日运行期望成本最小为目标,考虑分布式可再生能源发电不确定性,建立多阶段随机规划模型,可以根据之前不确定信息的实现在每个阶段确定预调度决策,不会受到未来不确定信息的影响,符合系统实际运行规律,满足非预期性。为了避免多阶段随机规划问题求解的维数灾难,采用随机对偶动态规划(stochastic dual dynamic programming, SDDP)算法进行求解。仿真结果表明,相比于传统的确定性模型,多阶段随机优化得到的最优调度决策树较之确定性优化得到的单一决策方案具有更广泛的决策空间,可以基于上一阶段不确定信息的实现和决策来更新调度决策,降低系统的运行成本。     

Design and evaluation of a hybrid system for detection and prediction of faults in electrical transformers

Transformers are the vital parts of an electrical grid system. A faulty transformer can destabilize the electrical supply along with the other devices of the transmission system. Due to its significant role in the system, a transformer has to be free from faults and irregularities. Dissolved Gas-in-oil Analysis (DGA) is a method that helps in diagnosing the faults present in an electrical transformer. This paper proposes a hybrid system based on Genetic Neural Computing (GNC) for analyzing and interpreting the data derived from the concentration of the dissolved gases. It is further analyzed and clustered into four subsets according to the standard C57.104 defined by IEEE using genetic algorithm (GA). The clustered data is fed to the neural network that is used to predict the different types of faults present in the transformers. The hybrid system generates the necessary decision rules to assist the system’s operator in identifying the exact fault in the transformer and its fault status. This analysis would then be helpful in performing the required maintenance check and plan for repairs.     

Online adaptive parameter identification of PMSM based on the dead-time compensation

This paper presents a method of online identification of the parameters of permanent magnet synchronous motors (PMSM) by model reference adaptive identification based on Popov Super Stability Theory. Firstly, the relations of parameters in the Field Orientation Control (FOC) system are analysed. Secondly, the proposed identification method of PMSM concerns two parts. In the case of high-speed operation of the motor, the method can accurately identify the inductance in dq-axis and the permanent magnet (PM) flux linkage. On the other hand, in the case of low speed, it can accurately identify the winding resistance of the stator. The method does not require additional excitation signals, but only makes use of motor voltage, current and their deviations. Thirdly, a simple and effective dead-time compensation method has been applied to inhibit the dead-time effects on the parameter identification. At last, the simulation and experiment results clearly demonstrate the validity and feasibility of the method.     

A new smart charging method for EVs for frequency control of smart grid

Nowadays, due to the shortage of fossil fuels on the one hand and their high prices on the other hand, using electric vehicles (EVs) has been increased. Charging of EVs has imposed new loads on power systems. These new and major loads have faced the frequency control and stability of power systems with new challenges. One way to deal with this new challenge is smart charging of EVs. In this method, grid condition is a key parameter that affects the charging of EV. In other words, in smart charging method, charging is performed with respect to power system parameters such as frequency. In this paper, a smart charging method based on fuzzy controller is proposed, in which charging process is performed with respect to the frequency deviation of grid and state of charge (SOC) of EV battery. To evaluate the performance of the proposed controller in control of grid frequency, IEEE 39-bus system in the presence of renewable energy sources is considered as test system. In order to the frequency analysis, this system is converted into a three-area system and, for each area, several EV categories with different numbers of EVs, battery capacity, start time of charging, and initial SOC are supposed. Moreover performance of proposed method is compared with an optimized PI controller in terms of frequency control. To investigate performance of proposed method in charging of EVs, a two area system is assumed and charging of EVs is verified by applying step loads to both areas. Simulations are carried out in MATLAB/SIMULINK environment. Results of the simulations reveal the good performance of the proposed controller in terms of frequency control of grid and charging of EVs.     

Graphene modified Co–B catalysts for rapid hydrogen production from NaBH4 hydrolysis

Graphene oxide (GO) modified Co–B catalysts for NaBH₄ hydrolysis have been synthesized by the chemical reduction in this work. The structural features and catalytic performance of as-prepared samples have been investigated and discussed as a function of amounts of GO. According to structure characterization, the catalysts still retain the amorphous structure of Co–B alloy with the addition of GO, while GO exists as reduced GO (r-GO). The textural analysis and morphology observation indicate that the appropriate amount of GO in Co–B catalyst results in the obvious increase of specific surface area and uniform clustered morphology, which contributes to improve active surface area for catalytic reactions. The results of surface species characterization show that the electron density at active Co sites increases due to an electron transfer from B to Co facilitated by r-GO. It has been found that 50 mg GO modified Co–B catalyst exhibits especially high activity with a hydrogen generation rate of 14.34 L min⁻¹·g_catalyst⁻¹ and much lower activation energy of 26.2 kJ mol⁻¹ for hydrolysis reaction of NaBH₄. Meanwhile, the reusability evaluations show that the catalyst preserves high stability which can still maintain 81.5% of its initial activity after 5 catalytic cycles.