Creating Temporally Correlated High-Resolution Power Injection Profiles Using Physics-Aware GAN

Traditional smart meter measurements lack the granularity needed for real-time decision-making. To address this practical problem, we create a generative adversarial networks (GAN) model that enforces temporal consistency on its high-resolution outputs via hard inequality constraints using a convex optimization layer. A unique feature of our GAN model is that it is trained solely on slow timescale aggregated power information obtained from historical smart meter data. The results demonstrate that the model can successfully create minutely interval temporally-correlated instantaneous power injection profiles from 15-minute average power consumption information. This innovative approach, emphasizing inter-neuron constraints, offers a promising avenue for improved high-speed state estimation in distribution systems and enhances the applicability of data-driven solutions for monitoring such systems.Comment: 5 page

Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

Smart Meter Data and Public Interest Issues– The National Perspective

It is expected that smart meters will be installed in approximately 27 million domestic properties and 2 million small business premises by end 2020. The data from these smart electricity and gas meters can help modernise the energy industry, facilitate the evolution of a smart grid in the UK and aid the development of new and innovative energy services for commercial organizations. However there are also public interest uses of smart meter data that could bring significant benefit to many stakeholder groups across the UK. This paper will examine some of the strengths and key limitations of existing energy datasets and highlight the benefits derived from smart meter data. Public interest uses of smart meter data in areas such as policy, academic research, the energy industry and benefits direct to the consumer will be discussed. The arrangements for accessing smart meter data are documented and potential barriers deriving from these arrangements are discussed. Finally recommendations that seek to address these barriers in areas such as policy and governance, data privacy and informed consent and data resources are provided. The need for a strong, co-ordinated voice for public interest perspectives amongst existing industry actors is highlighted

Creative User-Centered Visualization Design for Energy Analysts and Modelers

We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open – enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design

Connectivity Differences between Human Operators of Swarms and Bandwidth Limitations

Human interaction with robot swarms (HSI) is a young field with very few user studies that explore operator behavior. All these studies assume perfect communication between the operator and the swarm. A key challenge in the use of swarm robotic systems in human supervised tasks is to understand human swarm interaction in the presence of limited communication bandwidth, which is a constraint arising in many practical scenarios. In this paper, we present results of human-subject experiments designed to study the effect of bandwidth limitations in human swarm interaction. We consider three levels of bandwidth availability in a swarm foraging task. The lowest bandwidth condition performs poorly, but the medium and high bandwidth condition both perform well. In the medium bandwidth condition, we display useful aggregated swarm information (like swarm centroid and spread) to compress the swarm state information. We also observe interesting operator behavior and adaptation of operators' swarm reaction

Leveraging Optical Communication Fiber and AI for Distributed Water Pipe Leak Detection

Detecting leaks in water networks is a costly challenge. This article introduces a practical solution: the integration of optical network with water networks for efficient leak detection. Our approach uses a fiber-optic cable to measure vibrations, enabling accurate leak identification and localization by an intelligent algorithm. We also propose a method to access leak severity for prioritized repairs. Our solution detects even small leaks with flow rates as low as 0.027 L/s. It offers a cost-effective way to improve leak detection, enhance water management, and increase operational efficiency.Comment: Accepte

Optimisation of residential battery integrated photovoltaics system: analyses and new machine learning methods

Modelling and optimisation of battery integrated photovoltaics (PV) systems require a certain amount of high-quality input PV and load data. Despite the recent rollouts of smart meters, the amount of accessible proprietary load and PV data is still limited. This thesis addresses this data shortage issue by performing data analyses and proposing novel data extrapolation, interpolation, and synthesis models. First, a sensitivity analysis is conducted to investigate the impacts of applying PV and load data with various temporal resolutions in PV-battery optimisation models. The explored data granularities range from 5-second to hourly, and the analysis indicates 5-minute to be the most suitable for the proprietary data, achieving a good balance between accuracy and computational cost. A data extrapolation model is then proposed using net meter data clustering, which can extrapolate a month of 5-minute net/gross meter data to a year of data. This thesis also develops two generative adversarial networks (GANs) based models: a deep convolutional generative adversarial network (DCGAN) model which can generate PV and load power from random noises; a super resolution generative adversarial network (SRGAN) model which synthetically interpolates 5-minute load and PV power data from 30-minute/hourly data. All the developed approaches have been validated using a large amount of real-time residential PV and load data and a battery size optimisation model as the end-use application of the extrapolated, interpolated, and synthetic datasets. The results indicate that these models lead to optimisation results with a satisfactory level of accuracy, and at the same time, outperform other comparative approaches. These newly proposed approaches can potentially assist researchers, end-users, installers and utilities with their battery sizing and scheduling optimisation analyses, with no/minimal requirements on the granularity and amount of the available input data

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

Iot application for energy poverty detection based on thermal comfort monitoring

The development of a datalogger for identifying Energy Poverty (EP) using thermal comfort monitoring is described in this work. There is not a uniform definition of EP, and no global recommendations indicating the thermal comfort characteristics that should be utilized to identify EP. Most Internet of Things (IoT)-based systems designed for EP identification measure energy consumptions (electricity and gas). There is a lack of works that use IoT-based systems to identify EP through the monitoring of thermal comfort parameters. To address the deficiencies discovered in the identification of EP from the perspective of thermal efficiency, an IoT-based monitoring system was designed, developed, and tested. A first pilot was installed in a household in Getafe. A full month of temperature, relative humidity, and CO2 concentration measurements were utilized to evaluate the system, which was then compared to a commercial system. The results revealed that the new IoT-based approach was very dependable and may be used to accurately monitor EP-related parameters.This work was supported by the European Commission through Urban Innovative Actions of the EPIU Getafe Project under Grant UIA04-212. The work of Dr. Agapito Ledezma was supported by the Agencia Estatal de Investigación (AEI) under Grant PID2021-124335OB-C22