An Electricity Price-Aware Open-Source Smart Socket for the Internet of Energy

[Abstracts] The Internet of Energy (IoE) represents a novel paradigm where electrical power systems work cooperatively with smart devices to increase the visibility of energy consumption and create safer, cleaner and sustainable energy systems. The implementation of IoE services involves the use of multiple components, like embedded systems, power electronics or sensors, which are an essential part of the infrastructure dedicated to the generation and distribution energy and the one required by the final consumer. This article focuses on the latter and presents a smart socket system that collects the information about energy price and makes use of sensors and actuators to optimize home energy consumption according to the user preferences. Specifically, this article provides three main novel contributions. First, what to our knowledge is the first hardware prototype that manages in a practical real-world scenario the price values obtained from a public electricity operator is presented. The second contribution is related to the definition of a novel wireless sensor network communications protocol based on Wi-Fi that allows for creating an easy-to-deploy smart plug system that self-organizes and auto-configures to collect the sensed data, minimizing user intervention. Third, it is provided a thorough description of the design of one of the few open-source smart plug systems, including its communications architecture, the protocols implemented, the main sensing and actuation components and the most relevant pieces of the software. Moreover, with the aim of illustrating the capabilities of the smart plug system, the results of different experiments performed are shown. Such experiments evaluate in real-world scenarios the system’s ease of use, its communications range and its performance when using HTTPS. Finally, the economic savings are estimated for different appliances, concluding that, in the practical situation proposed, the smart plug system allows certain energy-demanding appliances to save almost €70 per yearGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; ED431C 2016-045Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; ED341D R2016/012Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; ED431G/01Agencia Estatal de Investigación; TEC2013-47141-C4-1-RAgencia Estatal de Investigación; TEC2015-69648-REDCAgencia Estatal de Investigación; TEC2016-75067-C4-1-

Residential Demand Side Management model, optimization and future perspective: A review

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints

Wirelessly Controlled Smart Outlet

Popularity of home automation devices has increased greatly in recent years due to higher affordability and simplicity through smartphone and tablet connectivity. For the purpose of this experiment, we have developed the Smart Outlet: a stand-alone communication unit, used to connect home outlets to the internet. The Smart Outlet controls lighting and simple appliances throughout the home, remotely, using wireless commands from the web. A Google Calendar interface directly controls the outlets’ function by scheduling them with dedicated calendar events. The calendar’s interface can even be accessed from multiple different platforms (i.e. phones, computers, tablets) for the convenience of all users. This paper presents the design and implementation of the Smart Outlet together with the calendar application to enable home outlets to receive commands and function on a scheduled basis

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Model Predictive Energy Management for Building Microgrids with IoT-based Controllable Loads

This thesis develops an economic scheduling framework for a building microgrid with internet of things (IoT) based flexible loads to synchronize the buildings’ controllable components, with occupant behavior and environmental conditions. We employ model predictive control (MPC) methods to minimize building operating costs, while maximizing the utilization of the on-site resources. The main research thrusts are: 1) Developing the building microgrid model; 2) Defining different building operation strategies; 3) Minimizing the building’s daily operating costs. Simulation results show that the proposed approach provides superior energy cost savings and peak load reduction in comparison with other operation controls, such as All from Utility (AFU), AFU with installed IoT-based Building Energy Management System (BEMS), and MPC-Mix Integer Linear Programming (MILP) without IoT-based BEMS. An economic analysis is also conducted to provide a road map for the implementation of installing advanced energy efficiency technologies across loads in building microgrid and integrating them with the building microgrid’s control strategy

Veracity in power consumption of smart home

This project is intended to develop for EDP Portugal from Cside for the newly launched smart home platform called EDP re:dy ( Remote Energy Dynamics) Home Automation devices with digital controlling platform from anywhere with optimum power consumption pattern in order to make energy efficient home.During the realization of this project,the veracity of re:dy smart meter has been analyzed with different load factors (capacitive, nductive) in order to observe the harmonics distortion pattern with various home appliances and traditional energy meters.Gathering all collected data in SDP (Service Delivery latform) online platform to analyze the sequence of energy values and according to that edp box will give effective power consumption pattern to the client's minimum tariff and energy saving pattern via reducing the power dissipation.As a final conclusion of the project, based on obtained results an effective method for smart home automation with edp re:dy box associated with MAC address of equipment added and controlled via online carrier/subscriber portal

Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households

Design and analysis of smart home energy management system for energy-efficient and demand response operations

In the movie “Iron Man”, Tony Stark, with his highly connected and smart home system, shows the audience an appealing vision of future work and domestic life. Many audiences desire such a living environment where they can not only interact with their homes but also let the homes manage their operation automatically. As technology progressively steps into such a future, realizing a responsive and autonomous smart home is not just a fantasy. To establish grid-interactive homes that help save costs for users and improve grid reliability, this study introduces an energy management framework for smart home environments. This framework provides optimal operation of multiple appliances, taking into account dynamic responses to external factors such as outside weather conditions, homeowner’s preferences, and particularly, gird conditions like time-varying pricing in demand response programs. As one of the largest energy consumers in the home, the operation of the HVAC system holds great potential for cost savings and energy flexibility—the latter being the ability to adjust its consumption based on grid signals such as time-of-use (TOU) pricing. Achieving cost savings and energy flexibility requires intelligent strategies, one of which is precooling—a control strategy where an air conditioner (AC) cools space when the electricity price is low to avoid expensive operation when the electricity price is high. In previous studies, Model Predictive Control (MPC)-based precooling strategies are typically analyzed through simulations, and field studies in residential buildings are quite limited. In this study, we developed an MPC agent and carried out extensive field tests on nine homes over a period of four months in Oklahoma and Miami. Filed test results show that the MPC agent can reduce energy cost by 28.72%–51.31% on hot summer days and by up to 60.32% on mild summer days, in addition to achieving significant energy flexibility. Moreover, the agent's performance is found to be most impacted by weather conditions, AC performance, user comfort preferences, and floor areas of the homes. In addition, to further comprehend diverse factors that may impact the results of MPC-based precooling, an EnegyPlus virtual testbed and a corresponding control framework for co-simulation are developed. The purpose of developing such a virtual testbed is to create a simulation environment that enables experiments without the limitation and variability of field tests. The virtual testbed is modified by using the Python script to mimic the on/off cycle in the majority of U.S. residential building HVAC systems. By conducting the sensitivity analysis and ablation study, the MPC-based precooling co-simulation results are evaluated. It was observed in our case study that cost savings achieved through MPC-based precooling were primarily influenced by the use of forecast weather. The accuracy of the models and the prediction horizon of the MPC models also plays a substantial but lesser extent role. With the optimal operation framework shifting from the HVAC system to multiple appliances, the proposed energy management framework has a broader scope, encompassing not only the HVAC system but also water heaters, non-thermal appliances, and the power flow between photovoltaics panel (PV), batteries, and the grid. Apart from the cost-savings and energy flexibility that can be achieved, the proposed framework also provides a more realistic simulation scenario by considering the user’s appliance time usage preference, water usage, and thermal comfort preferences. Finally, the framework also embedded multi-objective optimization to support the homeowner’s decision-making between cost saving and thermal comfort. Overall, this study aims to realize the optimal operation of various load-flexible resources under demand response programs in residential buildings. This study investigates the fundamental research for the investigation of methodologies to enhance and understand the interactions between buildings, homeowners, and the grid. Due to the flexibility of the model, this study can be adapted to other residential buildings and even in larger communities

Demand Response in Smart Grids

The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer

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