902 resultados para Energy Consumption Management System


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L’obiettivo di questa tesi è approfondire le competenze sulle funzionalità sviluppate nei sistemi SCADA/EMS presenti sul mercato, così da conoscerne le potenzialità offerte: tutte le conoscenze acquisite servono a progettare uno strumento di analisi dati flessibile e interattivo, con il quale è possibile svolgere analisi non proponibili con le altre soluzioni analizzate. La progettazione dello strumento di analisi dei dati è orientata a definire un modello multidimensionale per la rappresentazione delle informazioni: il percorso di progettazione richiede di individuare le informazioni d’interesse per l’utente, così da poterle reintrodurre in fase di progettazione della nuova base dati. L’infrastruttura finale di questa nuova funzionalità si concretizza in un data warehouse: tutte le informazioni di analisi sono memorizzare su una base dati diversa da quella di On.Energy, evitando di correlare le prestazione dei due diversi sottosistemi. L’utilizzo di un data warehouse pone le basi per realizzare analisi su lunghi periodi temporali: tutte le tipologie di interrogazione dati comprendono un enorme quantità d’informazioni, esattamente in linea con le caratteristiche delle interrogazioni OLAP

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Questo elaborato di tesi è nato con l'esigenza di sviluppare un nuovo modulo per la stima di variabili energetiche da inserire nel software On.Energy, per dare la possibilità a tutti gli utilizzatori di capire quanto i valori osservati nella realtà si discostino da un modello teorico appositamente creato, e fornire quindi un ulteriore strumento di analisi per mantenere sotto controllo il sistema nell'ottica del miglioramento continuo. Il risultato sarà uno strumento che verrà provato in via sperimentale sugli stabilimenti di due aziende leader in Italia in settori produttivi largamente differenti (Amadori - alimentare, Pfizer - farmaceutico), ma accomunati dalle esigenze di monitorare, analizzare e efficientare i consumi energetici.

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In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out.

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This paper studies the energy consumption and subsequent CO2 emissions of road highway transportation under three toll systems in Spain for four categories of vehicles: cars, vans, buses and articulated trucks. The influence of toll systems is tested for a section of AP-41 highway between Toledo and Madrid. One system is free flow, other is traditional stop and go and the last toll system operates with an electronic toll collection (ETC) technology. Energy consumption and CO2 emissions were found to be closely related to vehicle mass, wind exposure, engine efficiency and acceleration rate. These parameters affect, directly or indirectly, the external forces which determine the energy consumption. Reducing the magnitude of these forces through an appropriate toll management is an important way of improving the energy performance of vehicles. The type of toll system used can have a major influence on the energy efficiency of highway transportation and therefore it is necessary to consider free flow.

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Energy Efficiency is one of the goals of the Smart Building initiatives. This paper presents an Open Energy Management System which consists of an ontology-based multi-technology platform and a wireless transducer network using 6LoWPAN communication technology. The system allows the integration of several building automation protocols and eases the development of different kind of services to make use of them. The system has been implemented and tested in the Energy Efficiency Research Facility at CeDInt-UPM.

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E=MC³ Energy Equals Management's Continued Cost Concern, is an essay written by Fritz G. Hagenmeyer, Associate Professor, School of Hospitality Management at Florida International University. In the writing, Hagenmeyer initially tenders: “Energy problems in the hospitality industry can be contained or reduced, yielding elevated profits as a result of applied, quality management principles. The concepts, processes and procedures presented in this article are intended to aid present and future managers to become more effective with a sharpened focus on profitability.” This article is an overview of energy efficiency and the management of such. In an expanding energy consumption market with its escalating costs, energy management has become an ever increasing concern and component of responsible hospitality management, Hagenmeyer will have you know. “In endeavoring to "manage" on a day-to-day basis a functioning hospitality building's energy system, the person in charge must take on the role of Justice with her scales, attempting to balance the often varying comfort needs of guests and occupants with the invariable rising costs of energy utilized to generate and maintain such comfort conditions, since comfort is seen as an integral part of the "service," "product," or "price/value” perception of patrons,” says Hagenmeyer. In contrast to what was thought in the mid point of this century - that energy would be abundant and cheap - the reality has set-in that this is not the case; not by a long shot. The author wants you to be aware that energy costs in buildings are a force to be reckoned with; a major expense to be sure. “Since 1973, "energy-conscious design" has begun to become part of the repertoire of architects, design engineers, and construction companies,” Hagenmeyer states. “For instance, whereas office buildings of the early 1970s might have used 400,000 British Thermal Units (BTUs) per square foot year, new buildings are going up that use 55,000 to 65,000 BTUs per square foot year,” Hagenmeyer, like an incandescent bulb, illuminates you. Hagenmeyer references Robert E. Aulbach’s article - Energy Management – when informing you that the hospitality manager should not become complacent in addressing the energy cost issue, but should and must maintain a diligent focus on the problem. Hagenmeyer also makes reference to the Middle East War and to OPEC, and their influence on energy prices. In closing, Hagenmeyer suggests an - Energy Management Action Plan – which he outlines for you.

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Greenhouse production is a very important activity in the West region of Portugal, with an area of approximately 800 ha where the regular production consists in two crops per year, one in winter-spring and the other in summer-autumn. Many growers are now prepared to better exploit market opportunities, since they know that the big export window opportunity is from June to September, when the production is difficult in other regions of south due to high temperatures. Grower’s use new and more productive varieties, either in soil or hydroponic systems, mostly in unheated greenhouses, naturally ventilated, and equipped with modern fertigation systems. Greenhouse production causes some environmental impacts due to the high use of inputs. Several improvements in technologies and crop practices may contribute to increase the use efficiency of resources, decreasing the negative environmental impacts. Greenhouse vegetable production in Northern EU countries is based on the supply of heating and differs significantly from the production system in the Southern EU countries. In the Northern countries, direct energy inputs, mostly for heating, are predominant while in the South the indirect energy input is also important, mainly associated with fertilizers, plastic cover materials and other auxiliary materials. The main objective of this work was to characterise the greenhouse production systems in the West region of Portugal, in order to evaluate the energetic consumptions (direct and indirect), the GHH emissions, the production costs and the farmer’s income. With this work the mostly important inputs were identified, allowing proposing alternative measures to improve efficiency and sustainability. All the data was obtained by surveys performed directly with growers, previously selected to be representative of the crop practices and greenhouse type of the region. However, more research should be performed in order to develop and to test technologies capable to improve resources use efficiency in greenhouse production.

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The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

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The smart grid concept appears as a suitable solution to guarantee the power system operation in the new electricity paradigm with electricity markets and integration of large amounts of Distributed Energy Resources (DERs). Virtual Power Player (VPP) will have a significant importance in the management of a smart grid. In the context of this new paradigm, Electric Vehicles (EVs) rise as a good available resource to be used as a DER by a VPP. This paper presents the application of the Simulated Annealing (SA) technique to solve the Energy Resource Management (ERM) of a VPP. It is also presented a new heuristic approach to intelligently handle the charge and discharge of the EVs. This heuristic process is incorporated in the SA technique, in order to improve the results of the ERM. The case study shows the results of the ERM for a 33-bus distribution network with three different EVs penetration levels, i. e., with 1000, 2000 and 3000 EVs. The results of the proposed adaptation of the SA technique are compared with a previous SA version and a deterministic technique.

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Smart grids are envisaged as infrastructures able to accommodate all centralized and distributed energy resources (DER), including intensive use of renewable and distributed generation (DG), storage, demand response (DR), and also electric vehicles (EV), from which plug-in vehicles, i.e. gridable vehicles, are especially relevant. Moreover, smart grids must accommodate a large number of diverse types or players in the context of a competitive business environment. Smart grids should also provide the required means to efficiently manage all these resources what is especially important in order to make the better possible use of renewable based power generation, namely to minimize wind curtailment. An integrated approach, considering all the available energy resources, including demand response and storage, is crucial to attain these goals. This paper proposes a methodology for energy resource management that considers several Virtual Power Players (VPPs) managing a network with high penetration of distributed generation, demand response, storage units and network reconfiguration. The resources are controlled through a flexible SCADA (Supervisory Control And Data Acquisition) system that can be accessed by the evolved entities (VPPs) under contracted use conditions. A case study evidences the advantages of the proposed methodology to support a Virtual Power Player (VPP) managing the energy resources that it can access in an incident situation.

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Energy Resources Management can play a very relevant role in future power systems in SmartGrid context, with high penetration of distributed generation and storage systems. This paper deals with the importance of resources management in incident situation. The system to consider a high penetration of distributed generation, demand response, storage units and network reconfiguration. A case study evidences the advantages of using a flexible SCADA to control the energy resources in incident situation.

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In the energy management of a small power system, the scheduling of the generation units is a crucial problem for which adequate methodologies can maximize the performance of the energy supply. This paper proposes an innovative methodology for distributed energy resources management. The optimal operation of distributed generation, demand response and storage resources is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The paper deals with a vision for the grids of the future, focusing on conceptual and operational aspects of electrical grids characterized by an intensive penetration of DG, in the scope of competitive environments and using artificial intelligence methodologies to attain the envisaged goals. These concepts are implemented in a computational framework which includes both grid and market simulation.

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The introduction of electricity markets and integration of Distributed Generation (DG) have been influencing the power system’s structure change. Recently, the smart grid concept has been introduced, to guarantee a more efficient operation of the power system using the advantages of this new paradigm. Basically, a smart grid is a structure that integrates different players, considering constant communication between them to improve power system operation and management. One of the players revealing a big importance in this context is the Virtual Power Player (VPP). In the transportation sector the Electric Vehicle (EV) is arising as an alternative to conventional vehicles propel by fossil fuels. The power system can benefit from this massive introduction of EVs, taking advantage on EVs’ ability to connect to the electric network to charge, and on the future expectation of EVs ability to discharge to the network using the Vehicle-to-Grid (V2G) capacity. This thesis proposes alternative strategies to control these two EV modes with the objective of enhancing the management of the power system. Moreover, power system must ensure the trips of EVs that will be connected to the electric network. The EV user specifies a certain amount of energy that will be necessary to charge, in order to ensure the distance to travel. The introduction of EVs in the power system turns the Energy Resource Management (ERM) under a smart grid environment, into a complex problem that can take several minutes or hours to reach the optimal solution. Adequate optimization techniques are required to accommodate this kind of complexity while solving the ERM problem in a reasonable execution time. This thesis presents a tool that solves the ERM considering the intensive use of EVs in the smart grid context. The objective is to obtain the minimum cost of ERM considering: the operation cost of DG, the cost of the energy acquired to external suppliers, the EV users payments and remuneration and penalty costs. This tool is directed to VPPs that manage specific network areas, where a high penetration level of EVs is expected to be connected in these areas. The ERM is solved using two methodologies: the adaptation of a deterministic technique proposed in a previous work, and the adaptation of the Simulated Annealing (SA) technique. With the purpose of improving the SA performance for this case, three heuristics are additionally proposed, taking advantage on the particularities and specificities of an ERM with these characteristics. A set of case studies are presented in this thesis, considering a 32 bus distribution network and up to 3000 EVs. The first case study solves the scheduling without considering EVs, to be used as a reference case for comparisons with the proposed approaches. The second case study evaluates the complexity of the ERM with the integration of EVs. The third case study evaluates the performance of scheduling with different control modes for EVs. These control modes, combined with the proposed SA approach and with the developed heuristics, aim at improving the quality of the ERM, while reducing drastically its execution time. The proposed control modes are: uncoordinated charging, smart charging and V2G capability. The fourth and final case study presents the ERM approach applied to consecutive days.

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It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.