Observability Analysis of Integrated Electricity and Heating Systems with Thermal Quasi-dynamics in Pipelines

Observability analysis(OA)is vital to obtaining the available input measurements of state estimation(SE)in an integrated electricity and heating system(IEHS).Considering the thermal quasi-dynamics in pipelines,the measurement equations in heating systems are dependent on the estimated results,leading to an interdependency between OA and SE.Conventional OA methods require measurement equations be known exactly before SE is performed,and they are not applicable to IEHSs.To bridge this gap,a scenario-based OA scheme for IEHSs is devised that yields reliable analysis results for a predefined set of time-delay scenarios to cope with this interdependency.As its core procedure,the observable state identification and observability restoration are formulated in terms of integer linear programming.Numerical tests are conducted to demonstrate the validity and superiority of the proposed formulation.

Superb creep lives of Ni-based single crystal superalloy through size effects and strengthening heterostructure γ/γ' interfaces

This study presents a design strategy to enhance the high-temperature creep resistance of Ni-based superalloys.This strategy focuses on two principles:(1)minimizing the dimensions ofγ/γ′interfaces andγchannels by reducing the size of theγ′phase;(2)key alloy composition control to strengthen the heterostructureγ/γ′interfaces.This strategy proved very effective by the designed three superalloys'prolonged creep lives.An alloy exhibits ultra-long creep life by 388 h at 1100°C/137 MPa,which runs at the highest level among those alloys without Ru addition.With Ru addition,an alloy that lasted for 748 h with a creep strain of~6%at 1110°C/137 MPa is developed.This study provides a new route of high-temperature creep lives through heterostructure interfacial design with size effects and key alloying elements.

Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation

Dynamic state estimation(DSE)accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time.This paper proposes a DSE approach for a doubly-fed induction generator(DFIG)with unknown inputs based on adaptive interpolation and cubature Kalman filter(AICKF-UI).DFIGs adopt different control strategies in normal and fault conditions;thus,the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases.Consequently,the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs,which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter.Furthermore,as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs,a large estimation error may occur or the DSE approach may diverge.To this end,in this paper,a local-truncation-error-guided adaptive interpolation approach is developed.Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can(1)effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient;(2)accurately track the dynamic states and unknown inputs of the DFIG;and(3)deal with various types of system operating conditions such as time-varying wind and different system faults.

Correction of Time-varying PMU Phase Angle Deviation with Unknown Transmission Line Parameters

Phasor measurement units(PMUs)provide useful data for real-time monitoring of the smart grid.However,there may be time-varying deviation in phase angle differences(PADs)between both ends of the transmission line(TL),which may deteriorate application performance based on PMUs.To address that,this paper proposes two robust methods of correcting time-varying PAD deviation with unknown parameters of TL(ParTL).First,the phenomena of time-varying PAD deviation observed from field PMU data are presented.Two general formulations for PAD estimation are then established.To simplify the formulations,estimation of PADs is converted into the optimal problem with a single ParTL as the variable,yielding a linear estimation of PADs.The latter is used by second-order Taylor series expansion to estimate PADs accurately.To reduce the impact of possible abnormal amplitude data in field data,the IGG(Institute of Geodesy&Geophysics,Chinese Academy of Sciences)weighting function is adopted.Results using both simulated and field data verify the effectiveness and robustness of the proposed methods.

Deep Reinforcement Learning Enabled Bi-level Robust Parameter Optimization of Hydropower-dominated Systems for Damping Ultra-low Frequency Oscillation

This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations(ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into a bi-level robust parameter optimization model. This allows us to consider a wide range of system operating conditions. To speed up the bi-level optimization process, the deep deterministic policy gradient(DDPG) based deep reinforcement learning algorithm is developed to train an intelligent agent. This agent can provide very fast lower-level decision variables for the upper-level model, significantly enhancing its computational efficiency. Simulation results demonstrate that the proposed method can achieve much better damping control performance than other alternatives with slightly degraded dynamic response performance of the governor under various types of operating conditions.

基于主题模型的微藻生物燃料产业链专利技术分析

【目的】基于主题模型挖掘微藻生物燃料产业链技术及技术继承关系。【方法】构建产业链模型,基于改进的LDA方法实现产业链环节–技术主题–专利映射;统计研发主体,分析技术发展趋势;构建基于语义相似度的专利加权引文网络,绘制产业链专利发展地图。【结果】在算法方面,基于短语抽取规则的LDA方法能够实现更精确的技术主题识别;在分析结果方面,得出微藻生物燃料产业链技术发展趋势,揭示产业链环节技术继承关系。【局限】主要针对微藻生物燃料产业链进行研究,建模方法若推广应用于其他产业,需要具有一定的目标产业背景知识。【结论】有效识别了微藻生物燃料产业链重点及热点环节,该产业链技术创新需多环节协同。

常压电喷雾离子化的机理及应用

作为最有前景的分析仪器之一,质谱技术已在药物、食品、环境、人类健康、国家安全及相关领域展现出广阔的应用前景。不同种类的分析物具有多种特征,这为直接离子化及质谱分析增加了难度。常压敞开式离子源是近年来新兴的一种离子源,这类离子源具有无需复杂的样品前处理、操作方便、快速、非破坏性、灵敏度及特异性好、能实现实时原位、高通量分析等特点。本文综述了基于电喷雾离子化(ESI)原理的各种离子源的电离机理、特征及应用,展望了常压敞开式离子源的发展趋势。

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

With the growing integration of distributed energy resources(DERs),flexible loads,and other emerging technologies,there are increasing complexities and uncertainties for modern power and energy systems.This brings great challenges to the operation and control.Besides,with the deployment of advanced sensor and smart meters,a large number of data are generated,which brings opportunities for novel data-driven methods to deal with complicated operation and control issues.Among them,reinforcement learning(RL)is one of the most widely promoted methods for control and optimization problems.This paper provides a comprehensive literature review of RL in terms of basic ideas,various types of algorithms,and their applications in power and energy systems.The challenges and further works are also discussed.

Distributed Real-time State Estimation for Combined Heat and Power Systems

This paper proposes a distributed real-time state estimation(RTSE)method for the combined heat and power systems(CHPSs).First,a difference-based model for the heat system is established considering the dynamics of heat systems.This heat system model is further used along with the power system steady-state model for holistic CHPS state estimation.A cubature Kalman filter(CKF)-based RTSE is developed to deal with the system nonlinearity while integrating both the historical and present measurement information.Finally,a multi-timescale asynchronous distributed computation scheme is designed to enhance the scalability of the proposed method for largescale systems.This distributed implementation requires only a small amount of information exchange and thus protects the privacy of different energy systems.Simulations carried out on two CHPSs show that the proposed method can significantly improve the estimation efficiency of CHPS without loss of accuracy compared with other existing models and methods.

Unified optimal power flow model for AC/DC grids integrated with natural gas systems considering gas-supply uncertainties

A unified optimal power flow(OPF) model for AC/DC grids integrated with natural gas systems is proposed for the real-time scheduling of power systems.Herein, the primary physical couplings underlying this coordinated system are modeled and investigated. In addition, the uncertainties of gas loads are considered when studying the role of gas supply for gas-fired units in power system operations. The nonlinear gas system constraints are converted to the second-order cone forms that allow for the use of the Benders decomposition techniques and the interior-point method to obtain the optimal solution. The numerical results of the modified IEEE 118-bus test system that integrates the Belgium 20-node natural gas system demonstrate the effectiveness of the proposed model. The effects of gas demand uncertainties on the optimal schedule of thermal generators are investigated as well.

A Hybrid Agent-based Model Predictive Control Scheme for Smart Community Energy System with Uncertain DGs and Loads

A multi-agent consensus-based market scheme is proposed for the cooperation of community and multiple microgrids(MGs)in a distributed,economic and hierarchal man-ner.The proposed community-based market framework with frequency regulation(FR)market is formulated as a two-level scheduling problem:the global decision-making process of community agent(CA)to participate in the FR market and the interaction and control process of local MGs to achieve collaboration in response to the global target with efficient pricing rules.Specifically,the model predictive control(MPC)is integrated with the consensus-based theory to allow MG to obtain an economic and reliable dispatch in the presence of uncertainties of distributed generators and loads.Thanks to the distributed nature of the proposed scheme,its robustness to communication issues has been strengthened and a win-win situation for all energy stakeholders can be achieved.The robustness of the proposed scheme is investigated in various conditions,including different implementation strategies,communication topologies,and the level of uncertainties.

Status of congenital extrahepatic portosystemic shunt

To the Editor:Studies have demonstrated that congenital extrahepatic portosystemic shunt(CEPS)is a serious and life-threatening disease.[1]According to statistics,deformity,infection,liver failure,and failure of other systems are the main causes of death.[2]This article will introduce the pathology,classification,clinical manifestations,diagnosis,and treatment of CEPS.