District Information Modeling and Energy Management

In recent years the European Commission enhanced strategies to promote ICTs for energy efficiency in buildings and cities. Within the Smart City context, energy-related information coming from different data-sources, either hardware or software needs to be integrated into a common smart digital archive for the city. We propose DIMMER, a distributed software infrastructure for district information modelling and energy management. It correlates energy-related information from different data-sources with user behaviour patterns and feedbacks. Hence, different actors playing in this scenario can access relevant information for providing new services and developing more efficient policies to enhance energy optimization in cities. This will provide support for strategic planning of the city and will foster the competition in the marketplace

Building performance simulation for sustainable building design and operation

This paper aims to provide a general view of the background and current state of building performance simulation, which has the potential to deliver, directly or indirectly, substantial benefits to building stakeholders and to the environment. However the building simulation community faces many challenges for the future. Several challenges relate to the need to provide better design support. Issues include early phase design support, multi-scale approaches (from construction detail to district level), uncertainty and sensitivity analysis, robustness analysis (employing use and environmental change scenarios), optimization under uncertainty, inverse approach (to address "how to" instead of being able to answer "what if" questions), multi-physics (particularly inclusion of electrical power flow modeling), and integration in the construction process (using building information modeling (BIM), process modeling, etc). Another group of challenges relates to the need to provide support for building operation and management. The issues include accurate in-use energy consumption prediction and model predictive control

Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

Locating a bioenergy facility using a hybrid optimization method

In this paper, the optimum location of a bioenergy generation facility for district energy applications is sought. A bioenergy facility usually belongs to a wider system, therefore a holistic approach is adopted to define the location that optimizes the system-wide operational and investment costs. A hybrid optimization method is employed to overcome the limitations posed by the complexity of the optimization problem. The efficiency of the hybrid method is compared to a stochastic (genetic algorithms) and an exact optimization method (Sequential Quadratic Programming). The results confirm that the hybrid optimization method proposed is the most efficient for the specific problem. (C) 2009 Elsevier B.V. All rights reserved

Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis

Smaller, Closer, Dirtier: Diesel Backup Generators in California

Quantifies the threat to air quality and human health by backup generators, and examines air quality in Los Angeles, San Diego, Sacramento, and Fresno, with some analysis of San Francisco as well

Load Balancing with Energy Storage Systems Based on Co-Simulation of Multiple Smart Buildings and Distribution Networks

In this paper, we present a co-simulation framework that combines two main simulation tools, one that provides detailed multiple building energy simulation ability with Energy-Plus being the core engine, and the other one that is a distribution level simulator, Matpower. Such a framework can be used to develop and study district level optimization techniques that exploit the interaction between a smart electric grid and buildings as well as the interaction between buildings themselves to achieve energy and cost savings and better energy management beyond what one can achieve through techniques applied at the building level only. We propose a heuristic algorithm to do load balancing in distribution networks affected by service restoration activities. Balancing is achieved through the use of utility directed usage of battery energy storage systems (BESS). This is achieved through demand response (DR) type signals that the utility communicates to individual buildings. We report simulation results on two test cases constructed with a 9-bus distribution network and a 57-bus distribution network, respectively. We apply the proposed balancing heuristic and show how energy storage systems can be used for temporary relief of impacted networks

Sustainable Strategic Urban Planning: Methodology for Urban Renovation At District Level

Sustainable urban renovation is characterized by multiple factors (e.g. technical, socio-economic, environmental and ethical perspectives), different spatial scales and a number of administrative structures that should address the evaluation of alternative scenarios or solutions. This defines a complex decision problem that includes different stakeholders where several aspects need to be considered simultaneously. In spite of the knowledge and experiences during the recent years, there is a need of methods that lead the decision-making processes. In response, a methodology based on the global idea and implications of working towards a more sustainable and energy efficient cities as a holistic procedure for urban renovation at district level is proposed in the European Smart City project CITyFiED. The methodology has the energy efficiency as main pillar and the local authorities as client. It is composed of seven phases that ensures an effective dialogue among all the stakeholders, aiming to understand the objectives and needs of the city to define a set of Strategies for Sustainable Urban Renovation and their integration within the Strategic Urban Planning of the cities.This project has received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement N° 609129. The authors would like to thank the rest of the partners of the CITyFiED project for their help and support

A review of microgrid development in the United States – A decade of progress on policies, demonstrations, controls, and software tools

Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries