Effects of Smart Charging of Multiple Electric Vehicles in Reducing Power Generation Fuel Cost

The high penetration of variable sources of renewable power generation will lead to operational difficulties in supply/demand balancing in the entire power system. The mass deployment of electric vehicles (EVs) and plug‐in hybrid vehicles (PHEVs) will also cause significant changes in electricity demand. Therefore, controlling and managing the charging time of EVs/PHEVs are effective approaches that are imperative for improving balancing in power system operation. We assumed travel patterns for EVs in a model of the future Tokyo power system and analyzed the power system loads, including the charging load of the EVs, under several charging control scenarios. We verified that charging time controls are substantially effective for reducing the fuel costs in the power system. Further, we found that load leveling under a multicar charging management scenario gave the best results in terms of the fuel costs in all cases.     

Aboveground tree additive biomass models in Ecuadorian highland agroforestry systems

Agroforestry land-use systems in the Andean region have great socioeconomical and biophysical relevance due to the abundance of products and services they provide. Biomass estimation in these systems constitutes a priority concern as it facilitates assessment of carbon sink potential and functionality for biomass production. In this paper, a set of equations were fitted to enable easy and reliable estimation of the total aboveground biomass of four frequently used species in Andean agroforestry systems: Acacia melanoxylon L., Alnus acuminata Kunth., Buddleja coriacea Remy. and Polylepis racemosa Ruiz&Pav. The best models for each biomass component (stem, thick branches, thin branches and leaves) per species were fitted simultaneously according to SUR methodology (seemingly unrelated regressions). All models showed high goodness of fit statistics and more than 70% of the observed variation in biomass components was explained by the independent variables. The inclusion of height as a predictive variable in the models improved their predictive reliability and expanded the application range. The models developed here are useful for assessing the sustainability of agroforestry systems and could support governmental or non-governmental forest conservation incentive programs and initiatives.     

Simultaneous process optimization and heat integration based on rigorous process simulations

This paper introduces a simultaneous process optimization and heat integration approach, which can be used directly with the rigorous models in process simulators. In this approach, the overall process is optimized utilizing external derivative-free optimizers, which interact directly with the process simulation. The heat integration subproblem is formulated as an LP model and solved simultaneously during optimization of the flowsheet to update the minimum utility and heat exchanger area targets. A piecewise linear approximation for the composite curve is applied to obtain more accurate heat integration results. This paper describes the application of this simultaneous approach for three cases: a recycle process, a separation process and a power plant with carbon capture. Case study results indicate that this simultaneous approach is relatively easy to implement and achieves higher profit and lower operating cost and, in the case of the power plant example, higher net efficiency than the sequential approach.     

Long-run urban dynamics: understanding local housing market change in London

Abstract

Recently, a literature has emerged using empirical techniques to study the evolution of international cities over many centuries; however, few studies examine long-run change within cities. Conventional models and concepts are not always appropriate and data issues make long-run neighbourhood analysis particularly problematic. This paper addresses some of these points. First, it discusses why the analysis of long-run urban change is important for modern urban policy and considers the most important concepts. Second, it constructs a novel data set at the micro level, which allows consistent comparisons of London neighbourhoods in 1881 and 2001. Third, the paper models some of the key factors that affected long-run change, including the role of housing. There is evidence that the relative social positions of local urban areas persist over time but, nevertheless, at fine spatial scales, local areas still exhibit change, arising from aggregate population dynamics, from advances in technology, and also from the effects of shocks, such as wars. In general, where small areas are considered, long-run changes are likely to be greater, because individuals are more mobile over short than long distances. Finally, the paper considers the implications for policy.     

Control theory for stochastic distributed parameter systems,an engineering perspective

The main purpose of this paper is to survey some recent progresses on control theory for stochastic distributed parameter systems, i.e., systems governed by stochastic differential equations in infinite dimensions, typically by stochastic partial differential equations. We will explain the new phenomenon and difficulties in the study of controllability and optimal control problems for one dimensional stochastic parabolic equations and stochastic hyperbolic equations. In particular, we shall see that both the formulation of corresponding stochastic control problems and the tools to solve them may differ considerably from their deterministic/finite-dimensional counterparts. More importantly, one has to develop new tools, say, the stochastic transposition method introduced in our previous works, to solve some problems in this field.     

The challenges of COVID-19 control policies for sustainable development of business: Evidence from service industries

Such large-scale disruptions as the pandemic increase the uncertainty and risk related to business. Therefore, the business continuity management (BCM) has become an essential technical solution for enterprise emergency response. Since the beginning of 2020, the COVID-19 has spread worldwide at an alarming rate causing many threats to sustainable development of the business sector. The decline in consumer demand has hugely impacted service industries, such as wholesale and retail sales, tourism. Enterprise production and operations have faced severe challenges. In this study, we develop a risk factor analysis of BCM under the presence of COVID-19 in China. Based on a statistical survey of 940 enterprises in Hangzhou City, China, this study employs ordinal logistic regression to explore the hindering effect of risk factors introduced by the epidemic on business performance. Then, the interpretive structure model (ISM) is applied to analyze the hierarchical structure of the factors under examination. The key factors influencing the enterprise production and operation during COVID-19 outbreak significantly differ across the sub-sectors of the service industry. Therefore, this paper assesses the resilience of the productive technologies and business models of different industries amid the pandemic. This paper proposes epidemic prevention and control strategy focusing on investment and government regulation to ensure sustainable business development.     

A rapid method to quantify casein in fluid milk by front-face fluorescence spectroscopy combined with chemometrics

Casein in fluid milk determines cheese yield and affects cheese quality. Traditional methods of measuring casein in milk involve lengthy sample preparations with labor-intensive nitrogen-based protein quantifications. The objective of this study was to quantify casein in fluid milk with different casein-to-crude-protein ratios using front-face fluorescence spectroscopy (FFFS) and chemometrics. We constructed calibration samples by mixing microfiltration and ultrafiltration retentate and permeate in different ratios to obtain different casein concentrations and casein-to-crude-protein ratios. We developed partial least squares regression and elastic net regression models for casein prediction in fluid milk using FFFS tryptophan emission spectra and reference casein contents. We used a set of 20 validation samples (including raw, skim, and ultrafiltered milk) to optimize and validate model performance. We externally tested another independent set of 20 test samples (including raw, skim, and ultrafiltered milk) by root mean square error of prediction (RMSEP), residual prediction deviation (RPD), and relative prediction error (RPE). The RMSEP for casein content quantification in raw, skim, and ultrafiltered milk ranged from 0.12 to 0.13%, and the RPD ranged from 3.2 to 3.4. The externally validated error of prediction was comparable to the existing rapid method and showed practical model performance for quality-control purposes. This FFFS-based method can be implemented as a routine quality-control tool in the dairy industry, providing rapid quantification of casein content in fluid milk intended for cheese manufacturing.     

基于边缘和局部熵融合的视觉安全性评估方法

针对基于局部熵进行加密图像视觉安全性评估存在块效应的局限性，引入图像的边缘特征，通过共有边缘来衡量加密图像与原始图像的边缘相似度，消除了块效应。由于局部熵对加密等级低的图像不敏感，边缘相似度对加密等级高的图像不敏感，将两个评估方法进行自适应融合，提出[SLEES]（Local Entropy and Edge Similarity，[SLEES]）指标。通过改变图像像素位置和图像像素值的加密方式处理图像和视频帧进行测试，实验结果表明，[SLEES]指标相比传统评估指标有更好的鲁棒性，评估范围更广。     

Reference governors and maximal output admissible sets for linear periodic systems

ABSTRACT

In this paper, we consider the problem of constraint management in Linear Periodic (LP) systems using Reference Governors (RG). First, we introduce the periodic-invariant maximal output admissible sets for LP systems. We extend the earlier results in the literature to Lyapunov stable LP systems with output constraints, which arise in RG applications. We show that, while the invariant sets for these systems may not be finitely determined, a finitely-determined inner approximation, which is periodically invariant, can be obtained by constraint tightening. We then analyze the geometric and algebraic relationship between these sets and show that these sets are related via simple transformations, implying that it suffices to compute only one of them for real-time applications. This greatly reduces the memory burden of RG (or other similar constraint management strategies), at the expense of an increase in processing requirements. We present a thorough analysis of this trade-off. In the second part of this paper, we present two RG formulations, and discuss their properties and algorithms for their computation. Numerical simulations demonstrate the efficacy of the approach.