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

The Role of Behavioural Economics in Energy and Climate Policy

This article explores how behavioural economics can be applied to energy and climate policy. We present an overview of main concepts of behavioural economics and discuss how they differ from the assumptions of neoclassical economics. Next, we discuss how behavioural economics applies to three areas of energy policy: (1) consumption and habits, (2) investment in energy efficiency, and (3) provision of public goods and support for pro-environmental behaviour. We conclude that behavioural economics seems unlikely to provide the magic bullet to reduce energy consumption by the magnitude required by the International Energy Agency's “450” climate policy scenario. However it offers new suggestions as to where to start looking for potentially sustainable changes in energy consumption. We believe that the most useful role within climate policy is in addressing issues of public perception of the affordability of climate policy and in facilitating the creation of a more responsive energy demand, better capable of responding to weather-induced changes in renewable electricity supply

Implementation of cloud services by using real-time analysis to reduce energy consumption

Through the continual application of sensors, wireless networking, network communication and cloud computing technology, vast volumes of data are increasingly collected in the energy sector which needs to be utilized for effective management. In this project, the overall perspective is to analyze energy consumption data collected from households’ smart meters in London and combining it with the application of cloud data technology. I will explore and utilize a state-of-the-art cloud service infrastructure to analyze and make smart decisions on managing energy usage. There is interest in using data mining techniques and time series for machine learning modelling to deliver a predictive measurement approach for forecast consumption. The cloud service proposed is Amazon Web Services (AWS) which will be used as statistical data for daily energy use, it can collect, analyze, and implement machine learning models to learn a user’s behaviors and enhance energy efficiency by automatically alerting the user when necessary in real-time. There needs to be a warning mechanism such as a web-based and mobile application which can interact with users through energy dashboards and SMS/emails, that way alerting the user and utility companies on excess consumption which is recommended in this research

Fraud detection in energy consumption: a supervised approach

Data from utility meters (gas, electricity, water) is a rich source of information for distribution companies, beyond billing. In this paper we present a supervised technique, which primarily but not only feeds on meter information, to detect meter anomalies and customer fraudulent behavior (meter tampering). Our system detects anomalous meter readings on the basis of models built using machine learning techniques on past data. Unlike most previous work, it can incrementally incorporate the result of field checks to grow the database of fraud and non-fraud patterns, therefore increasing model precision over time and potentially adapting to emerging fraud patterns. The full system has been developed with a company providing electricity and gas and already used to carry out several field checks, with large improvements in fraud detection over the previous checks which used simpler techniques.Peer ReviewedPostprint (author's final draft

Exploring market designs for local energy markets : core functionalities and value proposition in the context of blockchain, IoT and prosumers

This dissertation aimed to assess the impact of innovative smart market solutions and Blockchain technology on achieving efficient localized energy markets. Trends suggest the future of renewable energy generation will involve a move away from centralized power plants, and towards a large number of smaller generation units, such as PV cells. There are clear synergies between the market dynamics of photovoltaic systems and Blockchain-enabled smart markets, which can be harnessed towards integrating new consumption patterns and energy sources, as well as connecting consumers. Successful business strategy to integrate these technologies can lead to market leadership in this new industry. Captivating consumers is a key determinant of success, and offering lower electricity prices a necessary condition. For such offering to be feasible, markets need to be more efficient, as smart microgrids are proving to be. Consequently, there came the interest to see how new local electricity markets could be set up, while taking advantage of decentralization. A peer-to-peer, auction-based, local energy market was idealized and various simulations of were ran with differing levels of participants and structure, to understand the impact on the price of electricity achieved by the market. Market size and structure were both shown to affect price at different magnitudes, suggesting an ideal setup of 25-40 participants with generation capabilities over 60% of demand. Further analysis was undertaken to understand the impact of smart meters and Blockchain integration in such a market. Afterwards, conclusions were compiled and recommendations provided for how to approach new practical implementations.Esta dissertação teve como objetivo avaliar o impacto de inovadoras soluções de mercados inteligentes e tecnologia Blockchain em mercados locais de energia. Tendencias apontam para que o futuro das energias renovaveis passe por uma maior prevalencia de paineis fotovoltaicos domesticos. As sinergias entre as atuais dinamicas em mercados eletricos e o uso da Blockchain em mercados inteligentes parecem claras, podendo ser aproveitaveis para integrar novos perfis de consumo e conectar consumidores. Sendo um novo segmento, estratégias de mercado bem conseguidas serão essencias para ganhar posição, e a capacidade de angariar consumidores será um indicador crucial de sucesso. Para tal, os mercados têm que ser mais eficientes, algo que se tem revelado factual em casos de micro sistemas. Assim, criou-se o interesse de perceber como desenhar e implementar mercados localizados de energia que beneficiem desta tendencia de desintermediação. Para tal, um mercado interativo à base de leilões de eletricidade entre consumidores foi idealizado. Posteriormente, este foi simulado repetidamente, com diferentes dimensões e estruturas, a fim de perceber o seu impacto nos preços médios alcançados. Foi mostrado que tamanho e composição afetam os preços em magnitudes diferentes, sugerindo uma dimensão ideal de 25-40 participantes, com capacidades de autogeração superiores a 60%. Análises posteriors foram desenvolvidas de modo substantive, para avaliar o impacto de contadores eletricos inteligentes e integração da Blockchain neste tipo de mercado. Finalmente, conclusões foram reunidas e transformadas em recomendações para futuras implementações práticas

Emission-aware Energy Storage Scheduling for a Greener Grid

Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid's carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emission-aware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions, especially in the presence of intermittent renewables such as solar and wind. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid with 1,341 homes. Our results show a reduction of >0.5 million kg in annual carbon emissions -- equivalent to a drop of 23.3% in our electric grid emissions.Comment: 11 pages, 7 figure, This paper will appear in the Proceedings of the ACM International Conference on Future Energy Systems (e-Energy 20) June 2020, Australi

Fostering Household Energy Saving Behavior and Socialization of Smart Grid Technologies: Outcomes of a Utility Smart Grid Program

Smart metering and feedback technologies are designed to foster changes in demand side behavior. But the question, Do smart grids and smart technologies actually change behavior and promote more sustainable energy use? is yet to be answered—notably at the scale of a city. This study examines the way by which residential customers adopted and engaged with smart grid technologies, and the resulting changes in behavior from both these and pricing incentives from the utility. Data was obtained by analyzing a random sample of 240 respondents to three questionnaires (total n=1,303) implemented by a private sector consulting firm over summer in 2015 in Worcester, Massachusetts, USA where National Grid, is piloting a two-year smart grid project. Findings demonstrate that by creating a peak pricing scheme and diffusing household smart technologies, the program was able to foster an overall, modest reduction in energy consumption through energy saving behaviors