In last few years, many countries in the world have shown huge interest in smart grid technology. They are facing many challenges in the process of deployment of this technology at ground level. ...Hence a planned research is required to meet those challenges within time. This paper provides a detailed description of progress in the field of demand side management, demand response programs, distributed generation, technical issues in the way of their progress and key advantages, which will be received after the final deployment of these programs. Renewable energy resources are also becoming a main part of distributed generation, which provides a solution for environmental problems caused by conventional power plants. Few countries are working on the deployment of the advanced metering system. Along with this, the scope of research in various programs of smart grid technology has been explored.
With the advent of ICT in the energy system, new possibilities to inform and influence residential electricity consumption become available. We explore the potential of ICT-based interventions in ...households to decrease electricity usage, improve energy efficiency and thus contribute to reducing GHG (greenhouse gas) emissions from this sector. Based on a literature review on the subject, we suggest that ICT can affect some of the main behaviour-influencing factors, and discuss the causal avenues by which these effects can take hold. Our review finds that ICT-based effects on consumer behaviour can reduce household final electricity consumption by 0–5%. These and other findings from the literature are used to define parameter values, which reflect the efficacy of ICT at changing household energy usage patterns, and ultimately decreasing GHG emissions from the electricity sector. A quantitative analysis of the potential for ICT to contribute to reaching the 1.5 °C target in the context of the European Union (EU) energy sector is performed. It is found that ICT-based interventions in household energy use could contribute between 0.23% and 3.3% of the EU CO2e reduction target from the energy sector that would keep warming under 1.5 °C, corresponding to 4.5–64.7 mio. tCO2e abated per year.
•The ways that ICT can be used in the household electricity sector are summarized.•Proven effects of ICT on household electricity use are compiled.•Potential of ICT in electricity sector to reduce greenhouse gases is assessed.
With the advent of modern cognitive neuroscience and new tools of studying the human brain "live," music as a highly complex, temporally ordered and rule-based sensory language quickly became a ...fascinating topic of study. The question of "how" music moves us, stimulates our thoughts, feelings, and kinesthetic sense, and how it can reach the human experience in profound ways is now measured with the advent of modern cognitive neuroscience. The goal of Rhythm, Music and the Brain is an attempt to bring the knowledge of the arts and the sciences and review our current state of study about the brain and music, specifically rhythm. The author provides a thorough examination of the current state of research, including the biomedical applications of neurological music therapy in sensorimotor speech and cognitive rehabilitation. This book will be of interest for the lay and professional reader in the sciences and arts as well as the professionals in the fields of neuroscientific research, medicine, and rehabilitation.
In a smart grid, fine-grained usage reports of consumers are gathered using some computationally constrained smart measurement devices. One of the most challenging requirements in the data ...aggregation is how to securely read the consumption data, while putting the least possible overhead on smart meters. For this reason, recently, two efficient security protocols have been proposed to be used for subsequent secure consumption reports gathered from isolated smart measurement devices. Nonetheless, in both protocols, for each key establishment, the smart reader requires to connect to the electricity service provider (ESP) via the Internet. This paper proposes a novel key establishment protocol, which is free from the ESP involvement during the key agreement and benefits from notable reduction in the communication cost. Our thorough efficiency and security analyses indicate the eminence of the proposed security protocol.
The wide popularity of smart meters enables the collection of massive amounts of fine-grained electricity consumption data. Extracting typical electricity consumption patterns from these data ...supports the retailers in their understanding of consumer behaviors. In this way, diversified services such as personalized price design and demand response targeting can be provided. Various clustering algorithms have been studied for electricity consumption pattern extraction. These methods have to be implemented in a centralized way, assuming that all smart meter data can be accessed. However, smart meter data may belong to different retailers or even consumers themselves who are not willing to share their data. In order to better protect the privacy of the smart meter data owners, this paper proposes two federated learning approaches for electricity consumption pattern extraction, where the k-means clustering algorithm can be trained in a distributed way based on two frequently used strategies, namely model-averaging and gradient-sharing. Numerical experiments on two real-world smart meter datasets are conducted to verify the effectiveness of the proposed method.
The energy management system becomes increasingly indispensable with the extensive penetration of new players in the distribution networks, such as renewable energy, storage, and controllable load. ...Also, the operation optimization of the active distribution system requires information on operation state monitoring. Smart measuring equipment enables the topology identification and branch line parameters estimation from a data-driven perspective. Nevertheless, many current methods require the nodal voltage angles measured by phasor measurement units (PMUs), which might be unrealistic for conventional distribution networks. This paper proposes a numerical method to identify the topology and estimate line parameters without the information of voltage angles. We propose a two-step framework: the first step applies a data-driven regression method to provide a preliminary estimation on the topology and line parameter; the second step utilizes a joint data-and-model-driven method, i.e., a specialized Newton-Raphson iteration and power flow equations, to calculate the line parameter, recover voltage angle and further correct the topology. We test the method on IEEE 33 and 123-bus looped networks with load data from 1000 users in Ireland. The results demonstrate that the proposed method can provide an accurate estimation of the topology and line parameters based on limited samples of measurement without voltage angles.
Consumer acceptance of smart meters remains crucial in achieving the potential carbon emission reductions offered by advanced metering infrastructures. Given this, the present research used ...deliberative focus groups to examine what is needed to secure acceptance and engagement from domestic consumers with services, products and ‘offers’ in smarter power systems. Our findings suggest that consumers are able to identify not just threats relating to smart metering initiatives but opportunities as well. In particular, our focus group participants responded positively to the idea of an automated system that could be used to achieve energy savings in combination with time-of-use tariffs. We conclude by outlining suggestions for policy recommendations that may help consumer acceptance of smart meter enabled services be more readily achieved.
•We examine consumer acceptance of smart metering initiatives using focus groups.•Consumers perceive both threats and opportunities in smart metering initiatives.•Threats include; autonomy issues, privacy concerns and mistrust of suppliers.•Opportunities include: accurate billing and enablement of future ICT services.•Consumers responded positively to the idea of automated energy management.
Abstract
Water meters of different types and sizes are used to monitor and bill the water supply. Although the water is of drinking water quality, its chemo-physical properties often adversely affect ...the measuring behaviour of a meter after a while. There is thus the risk that they no longer meet legal requirements and may no longer be used. In this paper a test regime with a focus on pH, total hardness and particle load is presented which allows water meters to be tested closer to their operating conditions prior to placing them on the market. The regime goes beyond the conventional continuous durability test as described in OIML R49:2013(E) and ISO 4064:2014. The feasibility and reliability of the test regime has been demonstrated through implementation at different facilities. In the study, the measurement performance of water meters of various types and from different manufacturers was also investigated. A heterogeneous spread of measurement errors was found for both, water meters in mint conditions and those which were exposed to a defined water quality. Furthermore, compared to the conventional continuous durability test, the test regime developed in the study generally leads to stronger changes in the measurement error of the water meters.
Smart meters provide large amounts of data and the value of this data is getting increased attention because a better understanding of the characteristics of consumers helps utilities and retailers ...implement more effective demand response programs and more personalized services. This paper investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-demographic information about the consumers.
As the cost of the residential solar system decreases, rooftop photovoltaic (PV) has been widely integrated into distribution systems. Most rooftop PV systems are installed behind-the-meter (BTM), ...i.e., only the net demand is metered, while the native demand and PV generation are not separately recorded. Under this condition, the PV generation and native demand are invisible to utilities, which brings challenges for optimal distribution system operation and expansion. In this paper, we have come up with a novel two-layer approach to disaggregate the unknown PV generation and native demand from the known hourly net demand data recorded by smart meters: 1) At the aggregate level, the proposed approach separates the aggregate PV generation time series from the aggregate net demand time series for customers with PVs. 2) At the customer level, the separated aggregate-level PV generation is allocated to individual PVs. These two layers leverage the spatial correlations of native demand and PV generation, respectively. One primary advantage of our proposed approach is that it is more independent and practical compared to previous works because it does not require PV array parameters, meteorological data and previously recorded solar power exemplars. This paper has verified our proposed approach using real native demand and PV generation data.
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