Energy on demand: Efficient and versatile energy control system for home energy management

Abstract—We have been proposing the concept of “i-Energy ” as a new energy management scheme to realize efficient and versatile control of e-power flows among decentralized energy generation/storage devices and appliances in homes, offices, and neighboring communities. The i-Energy concept is best characterized by a novel energy control method named “Energy on Demand (EoD). ” The novelties of EoD rest in (1) the explicit demand-based power supply control, (2) the best-effort power distribution based on appliance priorities, and (3) the ceiling control of power consumption. With EoD, people can attain the guaranteed reduction of energy consumption without damaging their quality of lives. Moreover, when utility companies are allowed to set and modify ceiling values based on contracts with consumers, EoD systems work as smart demand response systems. This paper first describes the protocol for EoD: power request demands named "Quality of Energy (QoEn) " and appliance priority descriptions. Then, the demand mediation algorithm based on appliance priorities for a single power source is introduced. Experiments using real world everyday-life data in a smart apartment room demonstrated the effectiveness of EoD

Energy-Efficient Smart Home System: Optimization of Residential Electricity Load Management System

In this project, an algorithm for electrical load management in a hypothetical household setting is proposed, developed and simulated. There are two main goals for the algorithm; firstly, to minimize the total electricity cost when a variable pricing model is applied; secondly, to flatten the demand curve over 24 hours, which, when applied to real-life settings, will minimize investment costs for the utilities – including building more generation plants and transmission lines – as well as the total bill for customers. To simulate the algorithm, mathematical models for appliances are developed based on typical usage and operation patterns

System Design of Internet-of-Things for Residential Smart Grid

Internet-of-Things (IoTs) envisions to integrate, coordinate, communicate, and collaborate real-world objects in order to perform daily tasks in a more intelligent and efficient manner. To comprehend this vision, this paper studies the design of a large scale IoT system for smart grid application, which constitutes a large number of home users and has the requirement of fast response time. In particular, we focus on the messaging protocol of a universal IoT home gateway, where our cloud enabled system consists of a backend server, unified home gateway (UHG) at the end users, and user interface for mobile devices. We discuss the features of such IoT system to support a large scale deployment with a UHG and real-time residential smart grid applications. Based on the requirements, we design an IoT system using the XMPP protocol, and implemented in a testbed for energy management applications. To show the effectiveness of the designed testbed, we present some results using the proposed IoT architecture.Comment: 10 pages, 6 figures, journal pape

Optimal integration of a hybrid solar-battery power source into smart home nanogrid with plug-in electric vehicle

Hybrid solar-battery power source is essential in the nexus of plug-in electric vehicle (PEV), renewables, and smart building. This paper devises an optimization framework for efficient energy management and components sizing of a single smart home with home battery, PEV, and potovoltatic (PV) arrays. We seek to maximize the home economy, while satisfying home power demand and PEV driving. Based on the structure and system models of the smart home nanogrid, a convex programming (CP) problem is formulated to rapidly and efficiently optimize both the control decision and parameters of the home battery energy storage system (BESS). Considering different time horizons of optimization, home BESS prices, types and control modes of PEVs, the parameters of home BESS and electric cost are systematically investigated. Based on the developed CP control law in home to vehicle (H2V) mode and vehicle to home (V2H) mode, the home with BESS does not buy electric energy from the grid during the electric price's peak periods

Energy flexibility assessment of a multi agent-based smart home energy system

Power systems worldwide are complex and challenging environments. The increasing necessity for an adequate integration of renewable energy sources is resulting in a rising complexity in power systems operation. Multi-agent based simulation platforms have proven to be a good option to study the several issues related to these systems. In a smaller scale, a home energy management system would be effective for the both sides of the network. It can reduce the electricity costs of the demand side, and it can assist to relieve the grid congestion in peak times. This paper represents a domestic energy management system as part of a multi-agent system that models the smart home energy system. Our proposed system consists of energy management and predictor systems. This way, homes are able to transact with the local electricity market according to the energy flexibility that is provided by the electric vehicle, and it can manage produced electrical energy of the photovoltaic system inside of the home.his work has been supported by the European Commission H2020 MSCA-RISE-2014: Marie Sklodowska-Curie project DREAM-GO Enabling Demand Response for short and real- time Efficient And Market Based Smart Grid Operation - An intelligent and real-time simulation approach ref. 641794.info:eu-repo/semantics/publishedVersio

Smart homes : a domestic demand response and demand side energy management system for future smart grids

Abstract: Smart homes or the homes of the future will be equipped with advanced technologies for user comfort and entertainment. Intelligent systems will be available to ensure this comfort and reliability. With these technological advancements comes further energy management. The concept of domestic energy efficiency is a concern at present and will be, in the future. So how do we optimize homes and users as to how they conserve energy? Domestic user’s energy usage represents a large amount of total electricity demand. Typical home energy systems utilize a rudimentary form of energy efficiency and management. In this paper we look at a Demand Response and Demand side management system model to curb this situation. The demand response system is achieved by the utility turning on/off smart power plugs wirelessly throughout the home based on peak and off peak periods via communication through its smart grid. To help consumers shift their loads during these times, appliance power sources that can act autonomously based on wired or wireless signals received from the utility via its smart grid is required. Users in response to this, connect their appliances to these plugs by generating their own hierarchy system by prioritizing their appliance usage. Whereas the demand side management system allows users to manually configure dates and times for the turning on/off of the smart power plugs wirelessly through the user’s smart user interface. Therefore, an energy efficient future smart home that can save the user on monthly expenditure and save on energy simultaneously

Life Cycle Assessment of Home Smart Objects: Kitchen Hood Cases

Abstract Promoting a more sustainable and energy-saving economy is one of the main goal of the European Community. In this context, home appliance manufacturers are researching and developing more efficient and sustainable products. Home automation and smart objects, by implementing specific energy management strategies, can significantly reduce energy waste. This paper aims to investigate the benefits offered, in terms of environmental impacts, by a smart system for kitchen air treatment. The system is composed by two inter-connected smart devices: a kitchen hood and an additional aspiration system able to assure a constant indoor comfort minimizing energy consumption and heat losses. Three different configurations were analyzed and compared: conventional extractor kitchen hood, smart extractor kitchen hood, and smart filtrating kitchen hood with smart additional aspiration system. Results show that in comparison with a traditional hood, products equipped with smart devices present lower environmental impact, due to the optimization of their energy consumptions

Home energy management system over low-power narrowband PLC

The need for efficient use of energy has inspired intelligent load control strategies in the home area network (HAN) using the power of Information and Communication Technologies (ICT). This paper investigates the use of low-power Narrowband Power Line Communication (NPLC) to support home energy management system (HEMS). Compared with low power wireless systems, it will be shown that using low-power Narrowband PLC (NPLC), packet success rate can be improved by approximately 85.32%, 208% and 85.32% in dense, sparse and large networks respectively. These results imply that low power NPLC is a feasible alternative for HEMS where low power wireless network is limited or inadequate

A user behaviour-driven smart-home gateway for energy management

Current smart-home and automation systems have reduced generality and modularity, thus confining users in terms of functionality. This paper proposes a novel system architecture and describes the implementation of a user-centric smart-home gateway that is able to support home-automation, energy usage management and reduction, as well as smart-grid operations. This is enabled through a middleware service that exposes a control API, allowing the manipulation of the home network devices and information, irrespectively of the involved technologies. Additionally, the system places the users as the prime owners of their data, which in turn is expected to make them much more willing to install and cooperate with the system. The gateway is supported by a centralised user-centric machine-learning component that is able to extract behavioural patterns of the users and feed them back to the gateway. The results presented in this paper demonstrate the efficient operation of the gateway and examine two well-know machine learning algorithms for identifying patterns in the user’s energy consumption behaviour. This feature could be utilised to improve its performance and even identify energy saving opportunities

Design of ensemble forecasting models for home energy management systems

The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.info:eu-repo/semantics/publishedVersio