Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

Cloud computing for energy management in smart grid - an application survey

The smart grid is the emerging energy system wherein the application of information technology, tools and techniques that make the grid run more efficiently. It possesses demand response capacity to help balance electrical consumption with supply. The challenges and opportunities of emerging and future smart grids can be addressed by cloud computing. To focus on these requirements, we provide an in-depth survey on different cloud computing applications for energy management in the smart grid architecture. In this survey, we present an outline of the current state of research on smart grid development. We also propose a model of cloud based economic power dispatch for smart grid

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Optimization Under Uncertainty in Building Energy Management

The introduction of decentralized energy resources as well as energy storage systems to the energy system calls for new control and coordination mechanisms and systems. This is also true for buildings. An optimized operation of buildings comprising decentralized generation and energy storage systems can be achieved by a building energy management system. It controls and coordinates the operation of individual devices in a building\u27s energy system to achieve given goals, such as the increase of energy efficiency, the decrease of carbon emissions, the minimization of operating costs or the provision of demand response measures. This thesis picks up on this idea and extends the ongoing research by presenting an approach to the optimized operation of building energy systems that includes the uncertainties in the predictions of the future energy generation and consumption into the control scheme of a building energy management system. To do so, this thesis identified the use of a scenario-based consideration of the uncertainties to be best suited. The presented approach uses a rolling horizon optimization approach with a stochastic two-stage optimization problem, which considers several forecast scenarios in the optimization

Optimal Control of Hybrid Systems and Renewable Energies

This book is a collection of papers covering various aspects of the optimal control of power and energy production from renewable resources (wind, PV, biomass, hydrogen, etc.). In particular, attention is focused both on the optimal control of new technologies and on their integration in buildings, microgrids, and energy markets. The examples presented in this book are among the most promising technologies for satisfying an increasing share of thermal and electrical demands with renewable sources: from solar cooling plants to offshore wind generation; hybrid plants, combining traditional and renewable sources, are also considered, as well as traditional and innovative storage systems. Innovative solutions for transportation systems are also explored for both railway infrastructures and advanced light rail vehicles. The optimization and control of new solutions for the power network are addressed in detail: specifically, special attention is paid to microgrids as new paradigms for distribution networks, but also in other applications (e.g., shipboards). Finally, optimization and simulation models within SCADA and energy management systems are considered. This book is intended for engineers, researchers, and practitioners that work in the field of energy, smart grid, renewable resources, and their optimization and control

Management energií v Smart Home

Import 03/11/2016This thesis aims to illuminate possibilities of increasing efficient energy use by creating visualization of measured energy consumption for end-user with respect to KNX technology. The motivation of creating system of KNX devices for monitoring and controlling energy consumption was reasons of increased demand of usage mobile devices and promotion of energy saving by visualization and using renewable energy. In this thesis are described and considered several ways for energy management, first and the most important is visualization, as consumer is able to monitor and manage energy and electricity consumption via mobile device and PC that makes possible to motivate user for smart use of energy and set alarms of over limit energy consumption. By the other hand remote control is flexible, comfortable and the idea that they can manage lighting and heating via mobiles attracts people. Mobile control is best solution to avoid paying extra money for buying touch panels.Tato diplomová práce si klade za cíl osvětlit možnosti zvýšení efektivního využití energie vytvořením vizualizace měření spotřeby energie pro koncového uživatele prostřednictvím KNX technologie. Motivací k vytvoření systému ze zařízení KNX pro monitorování a ovládání spotřeby energie bylo zvýšení poptávky použití mobilních zařízení a podpora úspory energie pomocí vizualizace a využití obnovitelných zdrojů energie. V této diplomové práci je popsáno několik způsobů pro hospodaření s energií. První a nejvíce důležitá je vizualizace, takže je uživatel schopný monitorovat a řídit energii a spotřebu elektřiny prostřednictvím mobilního zařízení a počítače, což umožňuje jednak uživatele motivovat k inteligentnímu využití energie a také nastavit upozornění při nadměrné energetické spotřebě. Dálkové ovládání je flexibilní, pohodlné, a díky možností nastavení osvětlení a topení prostřednictvím mobilu, také pro uživatele velmi atraktivní. Mobilní kontrola je nejlepším řešením pro vyhnutí se zbytečnému placení za nákup dotykových panelů.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř

建物におけるダイナミックプライシング導入の総合評価に関する研究

Electricity dynamic pricing represents one of the most significant evolutionary developments in pricing systems as they enable the participation of both consumers and the power supply enterprises. This paper surveyed the development status and challenges of smart grids and dynamic pricing in main driver countries. Meanwhile, it summarizes the results from an exploratory analysis of about 200 households and 50 offices that took part in dynamic price experiment in Kitakyushu, Japan. Based on the above, the effects of dynamic pricing on the cost performance of buildings in present scenario and demand response scenario have been presented. What\u27s more, this paper also gives an analysis and evaluation of electricity dynamic pricing in different buildings which equipped with different energy saving technologies under the condition that if consumers do not well respond to dynamic pricing as anticipated.北九州市立大