A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids

We present a distributed computing architecture for smart grid management, composed of two applications at two different levels of the grid. At the high voltage level, we optimize operations using a stochastic unit commitment (SUC) model with hybrid time resolution. The SUC problem is solved with an asynchronous distributed subgradient method, for which we propose stepsize scaling and fast initialization techniques. The asynchronous algorithm is implemented in a high-performance computing cluster and benchmarked against a deterministic unit commitment model with exogenous reserve targets in an industrial scale test case of the Central Western European system (679 buses, 1037 lines, and 656 generators). At the distribution network level, we manage demand response from small clients through distributed stochastic control, which enables harnessing residential demand response while respecting the desire of consumers for control, privacy, and simplicity. The distributed stochastic control scheme is successfully tested on a test case with 10,000 controllable devices. Both applications demonstrate the potential for efficiently managing flexible resources in smart grids and for systematically coping with the uncertainty and variability introduced by renewable energy

Spatial Solutions and Solution Spaces: The use of Virtual and Augmented Reality in Design Exploration

The recent wave of Virtual and Augmented Reality (VAR) technologies has coincided with new technologies for processing, analyzing and evaluating large amounts of data. In general, the purpose of Data Visualization is to enable the user to discover and understand patterns in data. Good visualizations present large amounts of data in a way that is easily understood, and good interactive visualizations promote intuitive means of exploring relationships. Over the past few years many researchers have looked into the development of immersive Virtual Environment platforms for Big Data visualization, such as, iViz (Donalek et al, 2014) and the work carried out by Masters of Pie and Lumacode for the Big Data VR Challenge in 2016 (Lumapie, 2016). Filtering, combination and scaling have all been identified elsewhere as important interactive techniques used in contemporary data visualization (Olshannikova et al, 2015). Of these, scaling may be the most familiar to architects: for centuries, designers have attempted to experience architectural space in different scales simultaneously, by using models at different scales (Yaneva, 2005), and by employing various drawing techniques to achieve an embodied perception of the designed space. With the use of VAR technologies this becomes easier than ever. At the same time, designers increasingly must understand not just the experience of a design proposal but also the data associated with it

Influence of kNN-Based Load Forecasting Errors on Optimal Energy Production

This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast is used to compute the optimal on/off status and generation scheduling of the units. Finally, the influence of forecasting errors on both the status and generation level of the units over the scheduling period is studied

Short-term Building Energy Model Recommendation System: A Meta-learning Approach

High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which can recommend appropriate models to forecast the building energy profiles based on building characteristics. To bridge this research gap, we propose a meta-learning based framework, termed Building Energy Model Recommendation System (BEMR). Based on the building’s physical features as well as statistical and time series meta-features extracted from the operational data and energy consumption data, BEMR is able to identify the most appropriate load forecasting model for each unique building. Three sets of experiments on 48 test buildings and one real building were conducted. The first experiment was to test the accuracy of BEMR when the training data and testing data cover the same condition. BEMR correctly identified the best model on 90% of the buildings. The second experiment was to test the robustness of the BEMR when the testing data is only partially covered by the training data. BEMR correctly identified the best model on 83% of the buildings. The third experiment uses a real building case to validate the proposed framework and the result shows promising applicability and extensibility. The experimental results show that BEMR is capable of adapting to a wide variety of building types ranging from a restaurant to a large office, and gives excellent performance in terms of both modeling accuracy and computational efficiency

Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy

The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for short-term load forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a self-organizing map (SOM), a clustering of the previous partition via k-means algorithm, and finally demand forecasting for each cluster with a multilayer perceptron. Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola. (C) 2014 Elsevier Ltd. All rights reserved.Hernandez, L.; Baladron, C.; Aguiar, JM.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J. (2014). Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy. Energy. 75:252-264. doi:10.1016/j.energy.2014.07.065S2522647

Digital Territories and the Design Construction Continuum

The purpose of the paper is to bring together the two newly elaborated concepts of Digital Territories (DT) and Design Construction Continuum (DCC) in order to approach the design of evolving - intelligent environments.Digital Territories is a concept elaborated 2005 by a Core Expert Group, conceived as an ephemeral  Ambient Intelligence (AmI) space. DTs formed through the interconnection of physical objects embedding digital technologies, postulate the integration of the physical and the digital world, searching for operative definitions of new evolving in time functionalities. In DTis, bridges between the physical and the digital are discrete elements disposing of certain autonomy in their conception and internal structure. Bridges have to be designed and located. The DCC proposes to relate design, fabrication and construction through information networks (it is in fact a DT). Through the DCC approach, design information is becoming construction information and industrial fabrication information. The DCC has to integrate interaction design and respond to questions posed by DTs design.  DTs are integrated to DCC by constituting an intermediate level between building programming and design. Intelligent  Building Components, that is AmI components operating as bridges between the physical and the digital in Digital Territories formations, cooperating to develop swarm intelligence applications to architectural space, are elements managed by the DCC. DTis are about spaces communicating and the DCC is about communicating (design) space

Multi-Objective Optimization for Public Policy

Operations research has a storied history of tackling complex problems in public policy, ranging from vaccine distribution to the efficient design of public utility markets. The advent of "big data" analytics, machine learning, and scalable optimization has only expanded the field's impact, unlocking new research directions and application areas. What makes public policy a challenging domain is a combination of three factors: (i) policymakers must balance multiple objectives that often exist in tension, e.g., tradeoffs in efficiency and fairness; (ii) there are many stakeholders, with often disparate value judgments on how to best balance said objectives; and (iii) those stakeholders may not be technically fluent in analytics. This thesis develops multi-objective optimization methodologies to support policymakers in designing more efficient, fair, and inclusive policies. We apply our techniques to a range of problems in transplantation policy and public education. A core theme of our work is the need for interpretable decision-support tools, e.g., interactive applications and tradeoff curves, which are crucial in translating abstract policy tradeoffs into actionable insights. Our goal is to provide stakeholders, even those without technical expertise, with an understanding of the range of achievable policy outcomes, so that they can more effectively engage in the policymaking process. We emphasize applications of our work to real-world problems, including an extensive collaboration with the United Network for Organ Sharing (UNOS) to help develop a new national lung allocation policy, which is slated for implementation in 2023. Chapter 2 addresses a long-standing debate about geographic equity in organ allocation, by using multi-objective optimization to compare efficiency/fairness tradeoffs under different geographic distribution schemes. Chapter 3 introduces a novel optimization-based framework for "ethics-by-design" in scarce resource allocation, aiming to combine data modeling, shareholder input, and ethical theory into a unified approach for policy development in this area. Chapter 4 details our collaboration with UNOS policymakers to apply this framework towards the design of a new national lung allocation policy. Finally, Chapter 5 presents an empirical analysis of school assignment mechanisms for public school districts, investigating tradeoffs between satisfying student preferences and minimizing bus transportation costs.Ph.D

Modeling techniques for power system grounding systems

Ph.D.Athanasios P. Meliopoulo