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

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Internet of Things Based Technology for Smart Home System: A Generic Framework

Internet of Things (IoT) is a technology which enables computing devices, physical and virtual objects/devices to be connected to the internet so that users can control and monitor devices. The IoT offers huge potential for development of various applications namely: e-governance, environmental monitoring, military applications, infrastructure management, industrial applications, energy management, healthcare monitoring, home automation and transport systems. In this paper, the brief overview of existing frameworks for development of IoT applications, techniques to develop smart home applications using existing IoT frameworks, and a new generic framework for the development of IoTbasedsmart home system is presented. The proposed generic framework comprises various modules such as Auto-Configuration and Management, Communication Protocol, Auto-Monitoring and Control, and Objects Access Control. The architecture of the new generic framework and the functionality of various modules in the framework are also presented. The proposed generic framework is helpful for making every house as smart house to increase the comfort of inhabitants. Each of the components of generic framework is robust in nature in providing services at any time. The components of smart home system are designed to take care of various issues such as scalability, interoperability, device adaptability, security and privacy. The proposed generic framework is designed to work on all vendor boards and variants of Linux and Windows operating system

Cloud Energy Micro-Moment Data Classification: A Platform Study

Energy efficiency is a crucial factor in the well-being of our planet. In parallel, Machine Learning (ML) plays an instrumental role in automating our lives and creating convenient workflows for enhancing behavior. So, analyzing energy behavior can help understand weak points and lay the path towards better interventions. Moving towards higher performance, cloud platforms can assist researchers in conducting classification trials that need high computational power. Under the larger umbrella of the Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (EM)3 framework, we aim to influence consumers behavioral change via improving their power consumption consciousness. In this paper, common cloud artificial intelligence platforms are benchmarked and compared for micro-moment classification. The Amazon Web Services, Google Cloud Platform, Google Colab, and Microsoft Azure Machine Learning are employed on simulated and real energy consumption datasets. The KNN, DNN, and SVM classifiers have been employed. Superb performance has been observed in the selected cloud platforms, showing relatively close performance. Yet, the nature of some algorithms limits the training performance.Comment: This paper has been accepted in IEEE RTDPCC 2020: International Symposium on Real-time Data Processing for Cloud Computin

Design and implementation of multiprotocol framework for residential prosumer incorporation in flexibility markets

The growth of distributed renewable energy in the electrical grid presents challenges to its stability and quality. To address this at the local level, flexibility energy strategies emerge as an innovative technique. However, managing these strategies in residential areas becomes complex due to the unique characteristics of each prosumer. A major challenge lies in managing communication among diverse devices with different protocols. To address these issues, a comprehensive framework is designed and implemented to facilitate prosumers' integration in flexibility strategies, addressing communication at various levels. The effectiveness of the proposed framework is demonstrated through its implementation in a real smart home environment with diverse devices. The framework enables seamless integration and communication between IoT devices and IEC 61,850-compliant power devices. This research presents a novel approach to address the challenges of managing flexibility strategies in residential areas, providing a practical solution for prosumers to actively participate in optimizing energy consumption and enhancing the stability and quality of the electricity system amidst the growing integration of distributed renewable energy.</p

Composite - its endless journey.

A composite, in its general term, is a solid material that results when two or more different substances, each with its own characteristics and properties, are combined to create a new substance whose properties are superior to those of the original components in a specific application, Composites are of greatest use in the aerospace industry in which their stiffness, lightness, and heat resistance make them the materials of choice in reinforcing the engine cowls, wings, doors, and flaps of aircraft. Composite materials are also used in rackets and other sports equipment, in cutting tools, and in certain parts of automotive engines

Profit-driven planning and analysis of a WEEE recycling facility with a multi-period MILP model

Electronic waste is one of the fastest-growing waste streams in the world. The challenges associated with the recycling of Waste Electrical and Electronic Equipment (WEEE) represent both threats, as the improper disposal of this waste can harm the environment and human health, and opportunities, as this category of waste contains valuable and rare resources that can be recovered and repurposed, contributing to the circular economy. The EU is leading the way in improving the collection and treatment of WEEE, but this has not been sufficient to meet the targets set in its WEEE directive. Therefore, additional efforts must be made to ensure the costeffective and environmentally sound recycling of WEEE, both in the public and private sectors. In this thesis, we propose a multi-period MILP model for the planning of a WEEE recycling facility in Belgium and conduct various analyses to provide insights on what elements are the most crucial to the profitability of such a facility. The originality of our approach lies in the multi-period aspect of the model, and the addition of a limited amount of labour to be allocated to various labour-intensive tasks of WEEE recycling. Our main findings are that labour is the most critical resource, both in cost and utilization, such that the optimal quantity of WEEE to process is the one that results in complete utilization of labour, with little to no overtime. As such, the flexibility of labour, both in possible task allocation and overtime capabilities, is crucial to the proper functioning of the facility, especially when taking into account possible deviations from the optimal plan, caused by the heterogeneity of WEEE and other variations such as the timing of deliveries.nhhma

Development of a multi-energy residential service demand model for evaluation of prosumers’ effects on current and future residential load profiles for heat and electricity

The motivation of this thesis is to develop a multi-energy residential service demand (MESD) model. The approach is based on earlier modelling concepts. Electricity is simu- lated by the help of a first-order Markov-chain approach simulating pseudorandom solar irradiation data as well as occupancy patterns, which are matched to stochastically deter- mined electric appliance activities (McKenna et al., 2015; Richardson & Thomson, 2012). A lumped-parameter model simulating indoor temperatures is utilized to estimate space heating (SH) demand (Nielsen, 2005). Measurement data on domestic hot water (DHW) consumption in dwellings is analysed in order to implement a DHW model. The model generates output in 1-minute resolution. It features various possibilities of dwelling customization: Among others, number of residents, building physics, electric appliances and heating regime may be adjusted. An interface providing a link to the Cambridge Housing Model (DECC, 2012) is implemented, which supports automated re- trieval of relevant building parameters. Electricity and DHW demand values may also be extracted to be used for model calibration. The added value of this work is the implementation of a DHW model and the combination of above named approaches to an integrated multi-energy service demand model. The electricity model is enhanced by improving the calibration mechanism and increasing electric appliance variety. The SH model is extended by random heating regime genera- tion based on field data. The model features full year simulations incorporating seasonal effects on DHW and SH demand. In addition, seven representative archetypes have been developed, which allow for detailed investigation of load profiles for heat and electricity of representative UK dwellings. The model has a wide scope of application. It can be used to explore the impact of differ- ent dwelling configurations on load matching and grid interaction throughout the seasons. Synthetic energy service demand profiles may support research on the optimal configura- tion of on-site supply appliances such as mCHP, PV and heat pumps. Furthermore, the model allows for drawing conclusions on the net carbon emissions of a dwelling and for assessing energy-efficiency measures