考虑电池储能老化和需求侧响应的交直流混合配电网动态重构方法

现有的电池储能模型一方面认为电池的充放电功率和效率为固定不变的,另一方面有关电池储能的充放电过程对循环老化的影响缺少考虑,这与电池单元本体的非线性等效电路模型相违背。因此,考虑电池储能的循环老化成本、需求侧响应,提出了交直流混合配电网动态重构方法。通过对实际电池储能的充放电功率的运行区域进行采样,在凸包络的采样点集合中将非理想状态下的电池储能模型重构为线性模型。在此基础上,分别建立了基于累积吞吐电量和放电深度的循环老化成本模型,构建了综合成本最小化的交直流混合配电网动态重构模型。并采用混合整数二阶锥规划方法求解。在改进的33节点算例上仿真验证了所提基于放电深度的循环老化成本模型的经济性,分析了交直流混合配电网重构方法经济性的影响因素。     

计及线路传输能力的新能源电力系统灵活性评估及优化调度方法

新能源大规模接入电网对电力系统灵活调节能力带来巨大挑战,针对目前电力系统灵活性评估及优化调度集中于供需匹配而缺乏对线路传输能力的考虑,提出了计及线路传输能力的新能源电力系统灵活性评估及优化调度方法。首先,分析线路传输能力对灵活性需求实现的影响,并计及新能源及负荷的波动性和不确定性,量化节点灵活性需求及其在线路上的分配。然后,从资源灵活性裕量和线路灵活性传输裕量两个角度提出系统灵活性不足量评估指标,多方面评估系统灵活性。将该评估指标作为机会约束条件,构建考虑资源-线路双重裕量的优化调度模型。采用线性化手段和基于拉丁超立方抽样的机会约束确定性转化方法将优化调度模型化简为混合整数线性规划模型以降低求解难度。最后,采用改进IEEE39节点系统和某实际电网进行仿真验证,结果表明所提方法能够减轻线路阻塞,减少切负荷量,提高新能源消纳。     

Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review

Meteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO2 emissions. The structure of these technologies relies on the deep integration of advanced data-driven techniques which can ensure efcient energy generation, transmission, and distribution. After conducting thorough research for more than a decade, the concept of the smart grid (SG) has emerged, and its practice around the world paves the ways for efcient use of reliable energy technology. However, many developing features evoke keen interest and their improvements can be regarded as the next-generation smart grid (NGSG). Also, to deal with the non-linearity and uncertainty, the emergence of data-driven NGSG technology can become a great initiative to reduce the diverse impact of non-linearity. This paper exhibits the conceptual framework of NGSG by enabling some intelligent technical features to ensure its reliable operation, including intelligent control, agent-based energy conversion, edge computing for energy management, internet of things (IoT) enabled inverter, agent-oriented demand side management, etc. Also, a study on the development of data-driven NGSG is discussed to facilitate the use of emerging data-driven techniques (DDTs) for the sustainable operation of the SG. The prospects of DDTs in the NGSG and their adaptation challenges in real-time are also explored in this paper from various points of view including engineering, technology, et al. Finally, the trends of DDTs towards securing sustainable and clean energy evolution from the NGSG technology in order to keep the environment safe is also studied, while some major future issues are highlighted. This paper can ofer extended support for engineers and researchers in the context of data-driven technology and the SG.     

基于定频MPC的飞跨电容储能双向直流变换器低频波动功率抑制策略

为抑制直流母线低频波动功率对储能系统的影响,以飞跨电容双向直流变换器为研究对象,提出基于单目标定频模型预测控制(model predictive control, MPC)低频波动功率抑制策略。为降低传统MPC算法的运算负荷,通过对变换器数学模型进行分析,提出一种单目标定频MPC控制算法。该算法仅需通过电感电流单目标约束函数,即可实现对系统电流及双端飞跨电容电压的控制,无须对两端飞跨电容电压进行独立寻优,极大地降低了计算量。为实现对储能系统低频波动功率的抑制,引入低频波动功率抑制算法,通过与所提MPC算法的融合,使最终控制方案能够在实现储能控制目标的前提下,具备低频波动功率抑制能力,且保持良好的动态性能。通过搭建小功率实验平台对所提控制策略的有效性进行了验证。     

基于多分区自适应改进果蝇算法的风光燃储微电网协调控制策略

为了使微电网控制系统中PI控制器的参数能够更好地适应可再生能源的随机性和波动性,提出了基于自适应步长的四分区多策略果蝇优化算法(fruit fly optimization algorithm, FOA)对PI参数进行实时优化。首先,以风光燃储微电网不同微源控制系统中的变换器为控制对象,建立微电网整体控制系统模型,基于此模型实时调整PI参数。然后,根据不同果蝇个体的适应度值将果蝇种群分为4个区,同时考虑4个区果蝇收敛性以及多样性的差异,设计不同的自适应更新策略。最后,采用所提算法对各微源控制过程中的PI参数进行寻优,与其他3种智能算法进行对比,验证了所提算法的可行性和优越性。仿真结果表明,所提算法可以使系统变换器响应速度更快,输出更加稳定。     

基于超级电容的新能源惯量-阻尼-电抗综合模拟控制方案

光伏、风电等新能源发电大规模接入电网逐步替代传统同步机,导致电网惯量和阻尼水平不断下降,威胁电网安全稳定运行。针对上述问题,提出了一种基于超级电容的新能源惯量阻尼电抗综合模拟控制(composite inertia-damping-impedance emulation control, IDIE)方案。通过联立同步机转子运动方程和超级电容功率动态方程,并结合同步机功率传输方程,设计了通过控制超级电容有功功率以综合模拟同步机旋转质量、阻尼绕组和绕组电抗三者物理特性的核心算法。并利用小信号稳定性分析对方案关键参数进行优化。最后,基于控制器硬件在环试验在各种暂态工况下对IDIE方案进行了评估。结果表明,IDIE方案在灵活模拟同步机惯量的同时,可通过配置优化模拟阻尼和模拟电抗,保障频率稳定性,支撑电网动态运行。     

Ultrahigh Energy Storage Capacitors Based on Freestanding Single-Crystalline Antiferroelectric Membrane/PVDF Composites

Inorganic/organic dielectric composites are very attractive for high energy density electrostatic capacitors. Usually, linear dielectric and ferroelectric materials are chosen as inorganic fillers to improve energy storage performance. Antiferroelectric (AFE) materials, especially single-crystalline AFE oxides, have relatively high efficiency and higher density than linear dielectrics or ferroelectrics. However, adding single-crystalline AFE oxides into polymers to construct composite with improved energy storage performance remains elusive. In this study, high-quality freestanding single-crystalline PbZrO₃ membranes are obtained by a water-soluble sacrificial layer method. They exhibit classic AFE behavior and then 2D–2D type PbZrO₃/PVDF composites with the different film thicknesses of PbZrO₃ (0.1-0.4 µm) is constructed. Their dielectric properties and polarization response improve significantly as compared to pure PVDF and are optimized in the PbZrO₃(0.3 µm)/PVDF composite. Consequently, a record-high energy density of 43.3 J cm⁻³ is achieved at a large breakdown strength of 750 MV m⁻¹. Phase-field simulation indicates that inserting PbZrO₃ membranes effectively reduces the breakdown path. Single-crystalline AFE oxide membranes will be useful fillers for composite-based high-power capacitors.     

CoFEE: Context-aware futuristic energy estimation model for sensor nodes using Markov model and autoregression

Nowadays, the emerging internet of things (IoT) technology offers the connectivity and communication between all things (various objects/things, devices, actuators, sensors, and mobile devices) at anywhere and anytime. These devices have embedded environment monitoring capabilities (sensors) and significant computational responsibilities. Most of the devices are working by utilizing their limited resources such as energy, memory, and bandwidth. Obviously, battery power is a crucial factor in any network. It makes tedious overheads to the network operations. Prediction of the future energy of the devices could be more helpful for managing resources, connectivity, and communication between the devices in IoT and wireless sensor networks (WSNs). It also facilitates the reliable internet and network connection establishment to the nodes. Hence, this paper presents an energy estimation model to predict the future energy of devices using the Markov and autoregression model. The proposed model facilitates smarter energy management among internet-connected devices. Performance results show that the proposed method gives significant improvement compared with the neural network and other existing predictions. Further, the proposed model has very lower error performance metrics such as mean square error and computation overhead. The proposed model yields more perfect energy predictions for a node with 64% to 97% and 16% to 43% of higher prediction accuracy throughout the time series.     

Flexible Transparent Bifunctional Capacitive Sensors with Superior Areal Capacitance and Sensing Capability based on PEDOT:PSS/MXene/Ag Grid Hybrid Electrodes

Flexible transparent supercapacitors (FTSs) have aroused considerable attention. Nonetheless, balancing energy storage capability and transparency remains challenging. Herein, a new type of FTSs with both excellent energy storage and superior transparency is developed based on PEDOT:PSS/MXene/Ag grid ternary hybrid electrodes. The hybrid electrodes can synergistically utilize the high optoelectronic properties of Ag grids, the excellent capacitive performance of MXenes, and the superior chemical stability of PEDOT:PSS, thus, simultaneously demonstrating excellent optoelectronic properties (T: ≈89%, R_s: ≈39 Ω sq⁻¹), high areal specific capacitance, superior mechanical softness, and excellent anti-oxidation capability. Due to the excellent comprehensive performances of the hybrid electrodes, the resulting FTSs exhibit both high optical transparency (≈71% and ≈60%) and large areal specific capacitance (≈3.7 and ≈12 mF cm⁻²) besides superior energy storage capacity (P: 200.93, E: 0.24 µWh cm⁻²). Notably, the FTSs show not only excellent energy storage but also exceptional sensing capability, viable for human activity recognition. This is the first time to achieve FTSs that combine high transparency, excellent energy storage and good sensing all-in-one, which make them stand out from conventional flexible supercapacitors and promising for next-generation smart flexible energy storage devices.