Home energy management system : a home energy management system under different electricity pricing mechanisms

Masteroppgave i fornybar energi ENE 500 Universitetet i Agder 2014Peak demand is a severe problem in the electricity grid and it was solved by supply side management in the past. But nowadays the demand side management sources have drawn attention due to the economic and environmental constraints. Demand side management in the domestic sector can play an important role in reducing the peak demand on the power system network. It can help in reducing stress and overloading on the transmission and distribution lines. In many countries there are various demand response programs implemented for industrial and commercial loads. In these programs load control is primarily achieved by various types of pricing mechanisms. There are very few demand response programs in use for energy management in residential sector. Direct curtailment of the loads is the most popular method used to reduce the peak demand in the domestic sector. But by direct load control, customer comfort may be compromised. In contrast peak load reduction through load shifting can benefit both consumers and utilities. In order to analyze demand response in the domestic sector, it is important to understand physical based power intensive load models with an emphasis on water heater units, air conditioner units, clothes dryers and electric vehicles. In this work, these load models are developed considering thermodynamic principles of buildings as well as their built in technical parameters. With the development of smart grid systems specially in the distribution network and possibility of load modeling, there is a requirement of a domestic intelligent energy management algorithm. In this work, power intensive non-critical loads are managed through developed energy management system algorithm and these loads are water heater, air conditioning unit, clothes dryer and electric vehicle. With the introduction of electric vehicles, demand responses can be performed within home for avoiding any overloading problems in the distribution network as well as on power generation. Additionally, the electricity bill saving which can be gained through proposed energy management system is analyzed by considering different electricity pricing mechanisms. The highlight of the presented energy management system algorithm for home energy management is its capability to control the non-critical loads below specified peak demand limits by considering consumer behavior and priorities, giving consumers more flexibility in their operational time. Moreover, the results show that the electricity saving which can be gained through the proposed energy management system lies in a noticeably high range. It is expected that the research findings of this work can be beneficial to utilities in providing information of limits and scope of domestic demand responses. And also it is anticipated that the cost analysis carried out can be used to motivate the consumers towards demand response through the developed energy management system. Key words: Domestic demand response, Home energy management system (EMS), demand limits, non-critical loads, load priority, Time of Use pricing, Real Time Pricin

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe

Smart home energy management

The new challenges on Information and Communication Technologies (ICT) in Automatic Home Systems (AHS) focus on the methods useful to monitor, control, and optimize the data management flow and the use of energy. An AHS is a residential dwelling, in some cases with a garden or an outdoor space, equipped with sensors and actuators to collect data and send controls according to the activities and expectations of the occupants/users. Home automation provides a centralized or distributed control of electrical appliances. Adding intelligence to the home environment, it would be possible to obtain, not only excellent levels of comfort, but also energy savings both inside and outside the dwelling, for instance using smart solutions for the management of the external lights and of the garden

NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio