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The assessment of voltage stability margins is a promising direction for wide-area monitoring systems. Accurate monitoring architectures for long-term voltage instability are typically centralized and lack scalability, while completely decentralized approaches relying on local measurements tend towards inaccuracy. Here we present distributed linear algorithms for the online computation of voltage collapse sensitivity indices. The computations are collectively performed by processors embedded at each bus in the smart grid, using synchronized phasor measurements and communication of voltage phasors between neighboring buses. Our algorithms provably converge to the proper index values, as would be calculated using centralized information, but but do not require any central decision maker for coordination. Modifications of the algorithms to account for generator reactive power limits are discussed. We illustrate the effectiveness of our designs with a case study of the New England 39 bus system. 10 pages, submitted for publication

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

This paper extends the analysis of the stability of electromagnetic transient simulation algorithms to non-linear systems with switching elements and non-linear inductor branches. A theoretical analysis based on common quadratic Lyapunov function (CQLF) theory is used to investigate the stability of numerical algorithms for the simulation of lumped strictly passive switched circuits (LSPSC). It is proved that only when certain fundamental physical properties, i.e., passivity and invariance of Lyapunov energy function are satisfied, does the widely used trapezoidal method result in stable simulations of such networks for any time-step size. This is different from the simulation of linear time invariant (LTI) systems where any real world stable system has a stable simulation if an A-stable integration method (e.g., trapezoidal rule) is used. Subsequently, it is shown that the problem of simulating a piecewise linear inductor can be equivalent to simulating a LSPSC; and ergo its simulation with the trapezoidal rule is also stable.

Abstract Recharging infrastructure (RI) deployment plays a vital role in improving the public recharging availability for transport electrification. Decarbonizing transportation using low-emission electricity requires massive RI network. Even though the consumers are reluctant to purchase electric vehicles (EVs) until RIs are sufficiently placed, the investors are not willing to invest in RIs due to recharging demand uncertainties. Therefore, a scientific planning framework is needed to ensure the sustainable deployment of EV-RIs in complex networks. In this study, a lifecycle thinking-based multi-period infrastructure-planning framework is proposed to develop sustainable public EV-RIs in an urban context. This framework consists of a temporal model to find the dynamic EV-RI demands, a stochastic model to obtain travel distances, and a multi-objective optimization model to select the best desirable capacities and locations for potential EV-RIs. A case study of a typical medium-scale municipality in Canada was assessed using the proposed framework and validated using conventional infrastructure planning scenarios. The geo-processing data, regional travel behaviors, and recharging characteristics were used as model inputs. The results of the case study showed that the proposed framework can be used to estimate multi-period public recharging demands, minimize lifecycle costs, maximize service coverage and infrastructure utilization, and ensure reasonable paybacks compared to conventional planning approaches. Moreover, this framework can be used to compare different investment assistances, which are required in the early stages of the RI deployment process to encourage investors. Furthermore, government and private institutions can use this framework to identify recharging demands, permitting, and developing RIs in the long-run.

In recent years, power quality (PQ) disturbance data are increasingly applied to extract useful information about the condition of power systems, such as monitoring incipient equipment failures. A prerequisite for such applications is the ability for a PQ monitor to detect abnormal waveforms. In response to this need, a generic method for waveform abnormality detection is proposed in this paper. The proposed method has two unique features. First, abnormalities are detected by comparing the statistical distributions of waveform variations with and without disturbances. Kullback-Leibler divergence (KLD) is used to assess the difference of the distributions. An abnormality exists if the KLD is larger than a threshold. Second, current waveforms are used for detection since they are more sensitive to equipment conditions. The difficulty to set a proper threshold due to large variations of current values is overcome through the adoption of KLD as the distance measure and a systematic threshold selection scheme. The scheme maximizes the detection probability for a given false alarm probability. Field-measured data and simulated data are applied to verify the effectiveness of the method.

AbstractConcrete is a valuable construction material with high mechanical strength and durability, used extensively in the construction industry. It is produced by mixing sand, stones, cement, and water in different proportions depending on the desired quality of the final product. Water reducers are additional chemical ingredients used in concrete to reduce the quantity of water required in the concrete mixture. When added to concrete, water reducers increase the workability and flowability of concrete in the freshly mixed state and improve the mechanical strength and durability of the final hardened product. This review paper describes the different types and applications of concrete water reducers used in the construction industry including their working mechanisms and fluidity effects on concrete properties. It discusses the production of synthetic and bio‐based concrete water reducers and reviews the present challenges involved in the preparation of bio‐based concrete water reducers from renewable resources. © 2023 Society of Industrial Chemistry and John Wiley & Sons Ltd.

Abstract Bitumen recovery by steam-solvent coinjection involves the coupled thermal/compositional mechanisms for reduction of bitumen viscosity. Reliable design of such processes requires reservoir flow simulation based on a proper phase-behavior model so that the oleic-phase viscosity near the steam-chamber edge can be modeled reliably. However, the effect of bitumen characterization (e.g., the number of pseudo components used) on steam-solvent coinjection simulation has not been studied in detail, and can be realized only after running multiple reservoir simulations, which is time consuming. There are two main objectives in this paper. One is to develop a reliable method for bitumen characterization by improving the fluid characterization method that was recently developed based on perturbation from n-alkanes (PnA). The other is to develop a novel analytical method for assessing the sensitivity of a particular coinjection simulation to bitumen characterization without having to perform reservoir simulations. A simulation case study is given to validate this analytical method. A proper number of pseudo components for bitumen characterization cannot be determined without considering the effect of phase behavior on the oleic-phase viscosity at chamber-edge conditions in steam-solvent coinjection simulation. Results show that the analytical method developed in this research can detect the sensitivity of recovery simulation to bitumen characterization without performing multiple flow simulations using different sets of fluid models. The PnA-based method developed for bitumen characterization gives reliable predictions of phase behavior for bitumen/solvent mixtures with a small amount of experimental data.

A ring main unit (RMU) is used in the power distribution system to provide three-phase supply. This RMU needs proper grounding for reliable and smooth power supply to residential areas. In this paper, ground resistance of an RMU is measured using fall-of-potential method. The ground resistance is measured by Fluke 1625 Advanced earth tester equipment and the value is found to be 1.58 Ω. Soil resistivity is also measured near the RMU and calculated by CYME GRD software. The COMSOL Multiphysics software is used to simulate ground resistance, and the simulation results are found to be in good agreement while compared to the experimental results.