Adjustable Robust Two-Stage Polynomial Optimization with Application to AC Optimal Power Flow

In this work, we consider two-stage polynomial optimization problems under uncertainty. In the first stage, one needs to decide upon the values of a subset of optimization variables (control variables). In the second stage, the uncertainty is revealed and the rest of optimization variables (state variables) are set up as a solution to a known system of possibly non-linear equations. This type of problem occurs, for instance, in optimization for dynamical systems, such as electric power systems. We combine tools from polynomial and robust optimization to provide a framework for general adjustable robust polynomial optimization problems. In particular, we propose an iterative algorithm to build a sequence of (approximately) robustly feasible solutions with an improving objective value and verify robust feasibility or infeasibility of the resulting solution under a semialgebraic uncertainty set. At each iteration, the algorithm optimizes over a subset of the feasible set and uses affine approximations of the second-stage equations while preserving the non-linearity of other constraints. The algorithm allows for additional simplifications in case of possibly non-convex quadratic problems under ellipsoidal uncertainty. We implement our approach for AC Optimal Power Flow and demonstrate the performance of our proposed method on Matpower instances.Comment: 28 pages, 3 table

Unlocking the Spreadsheet Utility for Calculus: A Pure Worksheet Solver for Differential Equations

This paper presents a unique solver for nonlinear initial-boundary value partial differential equations (PDE) that integrates with Microsoft Excel as a pure math function. The solver receives via input arguments formulas, variables, and parameters for the PDE, and is executed as a regular formula command in a range of cells. The solver, utilizing the method of lines, evaluates to a formatted tabular solution suitable for direct plotting of snapshot or transient views. Design of the solver is made possible by bypassing restrictions that block a worksheet function from receiving and evaluating formulas while preserving its purity. Three examples are presented to demonstrate the merits of this unconventional solver design which shields the tedious algorithmic implementation details from the user, and greatly simplifies solving a PDE using an intuitive math function without any dialogues, macros or VBA programming

Investing in PV Systems utilizing Savings from Building Envelop Replacement by Sustainable Local Material: A Case Study in Lebanese Inland Region

In this work, we propose to reduce the initial building construction cost by using local low-embodied energy materials and use savings to offset the cost of renewable energy installations. A typical house in the desert climate of inland Lebanon is modeled using commercial software for a conventional construction case and when using local construction materials. The savings from envelop replacement were used to invest in installing a solar water heater (SWH), and a photovoltaic (PV) system as well as double glazed windows. This resulted in net energy savings up to 97% and 59% for single and double glazed windows, respectively. When further investment in the PV system is evaluated and optimized based on life cycle cost, the savings from covering the electrical load and selling to the grid decreased respectively to 27% and 75% in the case of single glazing and to 28% and 76% in the case of double glazing. Keywords: Local construction material; Initial construction cost; PV system design and optimization; Renewable Energy JEL Classifications: C61; C63; L94; Q20; Q2

Orthogonal variability modeling to support multi-cloud application configuration

Cloud service providers benefit from a vast majority of customers due to variability and making profit from commonalities between the cloud services that they provide. Recently, application configuration dimensions has been increased dramatically due to multi-tenant, multi-device and multi-cloud paradigm. This challenges the configuration and customization of cloud-based software that are typically offered as a service due to the intrinsic variability. In this paper, we present a model-driven approach based on variability models originating from the software product line community to handle such multi-dimensional variability in the cloud. We exploit orthogonal variability models to systematically manage and create tenant-specific configuration and customizations. We also demonstrate how such variability models can be utilized to take into account the already deployed application parts to enable harmonized deployments for new tenants in a multi-cloud setting. The approach considers application functional and non-functional requirements to provide a set of valid multi-cloud configurations. We illustrate our approach through a case study

Data Standardization for Smart Infrastructure in First-Access Electricity Systems

Recent developments in renewable energy and Information Technology (IT) fields made it easier to set up power systems at a smaller scale. This proved to be a turning point for developing First-Access Electricity Systems for the underserved locations around the world. However, there are planning and operation challenges due to lack of past data on such places. Deployment of IoT devices and proliferation of smart infrastructures with additional sensors will lead to tremendous opportunities for gathering very useful data. For different stakeholders to access and manage this data, trusted and standardized mechanisms need to be in place. Storing proper data in a well-structured common format allows for collaborative research across disciplines, large-scale analytics, and sharing of algorithms and methodologies, in addition to improved customer service. Data standardization plays a more vital role in the context of electricity access in underdeveloped countries, where there is no past data on generation or consumption as in utility grids. Data collected in a standard structure, be it for a short period of time, facilitates learning from the past experiences, monitoring the current projects and delivering better results in future endeavors. It will result in ways to better assist consumers and help the industry operate more efficiently by sharing data with different stakeholders. It can also enhance competition, thus making electricity accessible faster and to more people. The focus of this paper is data standardization for first-access electricity systems, in general, and renewable energy based microgrids, in particular, different data sources and ways the corresponding data can be exploited, technological and capacity constraints for storage of data, political and governance implications, as well as data security and privacy issues, are examined. The work presented here is relevant to different stake holders such as investors, public utilities, non-governmental organizations (NGOs) and communities. Using the data standardization approach developed here, it is possible to create a much-needed first-access electricity system database. This will provide an important resource for project developers and energy companies to assess the potential of a certain unelectrified site, estimating its demand growth in time and establishing universal control systems that can seamlessly communicate with different components

Tourist Shoppers’ Evaluation of Retail Service: A Study of Cross-Border versus International Outshoppers

This article extends the concept of customer perceived value (CPV) to the tourist outshopping context and explores the differences in antecedents and outcomes of CPV between cross-border and international outshoppers. A large-scale field survey in Hong Kong with cross-border outshoppers from mainland China and international shoppers from four Western countries (Australia, Canada, the United Kingdom, and the United States) shows that perceived product quality, risk, and value for money have a stronger effect on CPV for cross-border outshoppers, and employee service quality and lifestyle congruence for international outshoppers. CPV also has a stronger positive effect on satisfaction, word of mouth, and repeat purchase intentions for cross-border outshoppers, whereas satisfaction has a stronger positive impact on word of mouth and repeat purchase intentions for international outshoppers. We discuss the conceptual contribution and managerial implications of our findings for international retailers, researchers, and tourism organizations

The integrated stress response is tumorigenic and constitutes a therapeutic liability in KRAS-driven lung cancer.

The integrated stress response (ISR) is an essential stress-support pathway increasingly recognized as a determinant of tumorigenesis. Here we demonstrate that ISR is pivotal in lung adenocarcinoma (LUAD) development, the most common histological type of lung cancer and a leading cause of cancer death worldwide. Increased phosphorylation of the translation initiation factor eIF2 (p-eIF2α), the focal point of ISR, is related to invasiveness, increased growth, and poor outcome in 928 LUAD patients. Dissection of ISR mechanisms in KRAS-driven lung tumorigenesis in mice demonstrated that p-eIF2α causes the translational repression of dual specificity phosphatase 6 (DUSP6), resulting in increased phosphorylation of the extracellular signal-regulated kinase (p-ERK). Treatments with ISR inhibitors, including a memory-enhancing drug with limited toxicity, provides a suitable therapeutic option for KRAS-driven lung cancer insofar as they substantially reduce tumor growth and prolong mouse survival. Our data provide a rationale for the implementation of ISR-based regimens in LUAD treatment

A dynamic inequality generation scheme for polynomial programming

Hierarchies of semidefinite programs have been used to approximate or even solve polynomial programs. This approach rapidly becomes computationally expensive and is often tractable only for problems of small size. In this paper, we propose a dynamic inequality generation scheme to generate valid polynomial inequalities for general polynomial programs. When used iteratively, this scheme improves the bounds without incurring an exponential growth in the size of the relaxation. As a result, the proposed scheme is in principle scalable to large general polynomial programming problems. When all the variables of the problem are non-negative or when all the variables are binary, the general algorithm is specialized to a more efficient algorithm. In the case of binary polynomial programs, we show special cases for which the proposed scheme converges to the global optimal solution. We also present several examples illustrating the computational behavior of the scheme and provide comparisons with Lasserre’s approach and, for the binary linear case, with the lift-and-project method of Balas, Ceria, and Cornuejols