Enhanced Home Energy Management Scheme (EHEM) in Smart Grids

Wireless Sensor Networks (WSNs) have become one of the most important components that play a major role in home environment applications. It plays a major role in the creation and the development of smart home environments. Smart homes creates home area network (HAN) to be used in different applications including smart grids. In this paper, we propose an enhancement to in-Home Energy Management (iHEM) scheme, namely EHEM, to reduce energy consumption by shifting the residents’ demands to mid-peak or off-peak periods depending on the appliances priorities and delays. The proposed system handles challenging cases by using internal storage battery. The performance of the proposed system is compared against iHEM and the traditional iHEM scheme, based on the total cost of the power consumption. Obtained results show slight improvement over the existing iHEM schem

Système de management énergétique résidentiel prédictif sous critères technico-économique

Nous proposons dans ce papier les résultats d'optimisation temporelle d'un ensemble représentant un bâtiment, une production et un stockage locaux d'énergie sur une journée future. Ces travaux se basent sur des résultats de prédiction effectués auparavant. Nous considérons ainsi trois ensembles différents (maison, batterie et panneaux solaires) définis par les niveaux d'énergie mis en jeu, tirés de relevés de consommation et de production réels. A partir de ces systèmes initiaux, cinq fonctions objectifs sont testées concernant les échanges d'énergie avec le réseau, le facteur de forme de la courbe de charge et des prix de l'énergie à l'usage ou en temps réel. La comparaison de ces objectifs permettra à terme de valider l'usage de la prédiction sur le contrôle optimal les sources et charges non conventionnelles du réseau basse tension par la comparaison des fonctions objectifs ainsi que la définition de scénarios d'usage des services d'énergie liés aux trois composants modélisés. A terme, leur agrégation permettra un effet de foisonnement bénéfique au vue des résultats d'optimisation

Vehicle-to-anything application (v2anything app) for electric vehicles

This paper presents a mobile information system denominated as Vehicle-to-Anything Application (V2Anything App), and explains its conceptual aspects. This application is aimed at giving relevant information to Full Electric Vehicle (FEV) drivers, by supporting the integration of several sources of data in a mobile application, thus contributing to the deployment of the electric mobility process. The V2Anything App provides recommendations to the drivers about the FEV range autonomy, location of battery charging stations, information of the electricity market, and also a route planner taking into account public transportations and car or bike sharing systems. The main contributions of this application are related with the creation of an Information and Communication Technology (ICT) platform, recommender systems, data integration systems, driver profile, and personalized range prediction. Thus, it is possible to deliver relevant information to the FEV drivers related with the electric mobility process, electricity market, public transportation, and the FEV performance.Fundação para a Ciência e Tecnologia (FCT

Investigation of Electric Water Heaters as Demand Response Resources and Their Impact on Power System Operational Reliability

The electricity consumption has increased dramatically in past decades due to the improvement of people’s life standard and the increase of their incomes. Some uncertainties have occurred because of an increasing electricity consumption at the household level. As a result, the high power consumption of massive households will affect power system reliability. Recently, the traditional power grid is being transformed to the smart grid, which is an effective way to deal with these issues. The electricity utility could manage the demand side resources using different kinds of Demand Response (DR) methods. Residential resource is an important part besides industrial resource and commercial resource. With the deployment of Home Energy Management System (HEMS) and smart household devices, users’ behavior could be adjusted to respond to the utility signal. Electric Water Heaters (EWHs) account for a huge percentage of energy consumption among all the home appliances. Aggregated EWHs are idea candidates as demand response resources whose power consumption pattern can be modified because they not only consume lots of energy but also have heat storage capability. Therefore, EWHs can react to the optimal operation signal without affecting customers’ daily needs. In this way, electricity utility could treat EWHs as a kind of interruptible load to provide operating reserves to improve power system reliability. In this thesis, a Binary Particle Swarm Optimization (BPSO) algorithm is utilized to perform the optimization of EWHs. The goal of each EWH optimization using BPSO is to minimize the customers’ electricity cost. Therefore, Time-Of-Use (TOU) electricity rate is utilized as the DR incentive. Meanwhile, the customers’ daily need for hot water should be guaranteed, so a comfort level index is enforced in the optimization process. The thermal model of EWH and water usage profile are used to calculate the real-time hot water temperature. Aggregating thousands of EWHs will have positive influences on power system reliability when massive EWHs are utilized as interruptible loads. EWHs could compensate for the Unit Commitment Risk (UCR) considering the operating reserve capacity they can provide. The UCR reduction is used to calculate and analyze the influence of aggregated EWHs. A Reliability Test System is modified to test the capacity of aggregated EWHs in this study. Based on the simulation results, the proposed optimization strategy for EWHs is proved to be practical. The customers’ electricity bill has declined effectively and the user’s comfort level, considering different water temperature set point ranges, is ensured. This thesis provides a practicable scheme for residential customers to arrange their EWHs more reasonably. The simulation results show the aggregated EWHs’ load curve and indicate that the proposed method shifts aggregated EWHs load effectively during some peak hours. According to the calculation results of UCR reduction, the aggregated EWHs is turned out to be a great candidate for power system to improve the reliability during peak-hours

An energy-efficient smart comfort sensing system based on the IEEE 1451 standard for green buildings

In building automation, comfort is an important aspect, and the real-time measurement of comfort is notoriously complicated. In this paper, we have developed a wireless, smart comfort sensing system. The important parameters in designing the prevalent measurement of comfort systems, such as portability, power consumption, reliability, and system cost, were considered. To achieve the target design goals, the communication module, sensor node, and sink node were developed based on the IEEE1451 standard. Electrochemical and semiconductor sensors were considered for the development of the sensor array, and the results of both technologies were compared. The sensor and sink nodes were implemented using the ATMega88 microcontroller. Microsoft Visual Studio 2013 preview was used to create the graphical user interface in C#. The sensors were calibrated after the signal processing circuit to ensure that the standard accuracy of the sensor was achieved. This paper presents detailed design solutions to problems that existed in the literature.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7361hj201

Optimization of Residential Battery Energy Storage System Scheduling for Cost and Emissions Reductions

The introduction of dynamic electricity pricing structures such as Time of Use (TOU) rates and Day Ahead Pricing (DAP) in residential markets has created the possibility for customers to reduce their electric bills by using energy storage systems for load shifting and/or peak load shaving. While there are numerous system designs and model formulations for minimizing electric bills under these rate structures the use of these systems has the potential to cause an increase in emissions from the electricity system. The Increase in emissions is linked to the difference in fuel mix of marginal generators throughout the day as well as inefficiencies associated with energy storage systems. In this work a multi-objective optimization model is designed to optimize reduction in cost of electricity as well as reduction in carbon dioxide (CO2) emissions from the electricity used by residential customers operating a battery energy storage system under dynamic pricing structures. A total of 22 different regions in the US are analyzed. Excluding emissions from the model resulted in an annual increase of CO2 emissions in all but one region ranging from 60-2000kg per household. The multi-objective model could be used to economically reduce these additional emissions in most regions by anywhere from 5 – 1300kg of CO2 per year depending on the region. When using the multi-objective model several regions had a net decrease in CO2 emissions compared to not using a battery system but most had a net increase