Hasil untuk "Electricity"

S. Singh

Wanghuai Xu, Huanxi Zheng, Yuan Liu et al.

D. Balsalobre-lorente, M. Shahbaz, D. Roubaud et al.

M. Kabeyi, O. Olanrewaju

The greatest sustainability challenge facing humanity today is the greenhouse gas emissions and the global climate change with fossil fuels led by coal, natural gas and oil contributing 61.3% of global electricity generation in the year 2020. The cumulative effect of the Stockholm, Rio, and Johannesburg conferences identified sustainable energy development (SED) as a very important factor in the sustainable global development. This study reviews energy transition strategies and proposes a roadmap for sustainable energy transition for sustainable electricity generation and supply in line with commitments of the Paris Agreement aimed at reducing greenhouse gas emissions and limiting the rise in global average temperature to 1.5°C above the preindustrial level. The sustainable transition strategies typically consist of three major technological changes namely, energy savings on the demand side, generation efficiency at production level and fossil fuel substitution by various renewable energy sources and low carbon nuclear. For the transition remain technically and economically feasible and beneficial, policy initiatives are necessary to steer the global electricity transition towards a sustainable energy and electricity system. Large-scale renewable energy adoption should include measures to improve efficiency of existing nonrenewable sources which still have an important cost reduction and stabilization role. A resilient grid with advanced energy storage for storage and absorption of variable renewables should also be part of the transition strategies. From this study, it was noted that whereas sustainable development has social, economic, and environmental pillars, energy sustainability is best analysed by five-dimensional approach consisting of environmental, economic, social, technical, and institutional/political sustainability to determine resource sustainability. The energy transition requires new technology for maximum use of the abundant but intermittent renewable sources a sustainable mix with limited nonrenewable sources optimized to minimize cost and environmental impact but maintained quality, stability, and flexibility of an electricity supply system. Technologies needed for the transition are those that use conventional mitigation, negative emissions technologies which capture and sequester carbon emissions and finally technologies which alter the global atmospheric radiative energy budget to stabilize and reduce global average temperature. A sustainable electricity system needs facilitating technology, policy, strategies and infrastructure like smart grids, and models with an appropriate mix of both renewable and low carbon energy sources.

Jiawen Kang, Rong Yu, Xumin Huang et al.

P. Bergmann

N. Jones

Guorui Chen, Yongzhong Li, Michael Bick et al.

Textiles have been concomitant of human civilization for thousands of years. With the advances in chemistry and materials, integrating textiles with energy harvesters will provide a sustainable, environmentally friendly, pervasive, and wearable energy solution for distributed on-body electronics in the era of Internet of Things. This article comprehensively and thoughtfully reviews research activities regarding the utilization of smart textiles for harvesting energy from renewable energy sources on the human body and its surroundings. Specifically, we start with a brief introduction to contextualize the significance of smart textiles in light of the emerging energy crisis, environmental pollution, and public health. Next, we systematically review smart textiles according to their abilities to harvest biomechanical energy, body heat energy, biochemical energy, solar energy as well as hybrid forms of energy. Finally, we provide a critical analysis of smart textiles and insights into remaining challenges and future directions. With worldwide efforts, innovations in chemistry and materials elaborated in this review will push forward the frontiers of smart textiles, which will soon revolutionize our lives in the era of Internet of Things.

Oliver Schmidt, Sylvain Melchior, A. Hawkes et al.

Summary The future role of stationary electricity storage is perceived as highly uncertain. One reason is that most studies into the future cost of storage technologies focus on investment cost. An appropriate cost assessment must be based on the application-specific lifetime cost of storing electricity. We determine the levelized cost of storage (LCOS) for 9 technologies in 12 power system applications from 2015 to 2050 based on projected investment cost reductions and current performance parameters. We find that LCOS will reduce by one-third to one-half by 2030 and 2050, respectively, across the modeled applications, with lithium ion likely to become most cost efficient for nearly all stationary applications from 2030. Investments in alternative technologies may prove futile unless significant performance improvements can retain competitiveness with lithium ion. These insights increase transparency around the future competitiveness of electricity storage technologies and can help guide research, policy, and investment activities to ensure cost-efficient deployment.

W. Tushar, T. K. Saha, C. Yuen et al.

Peer-to-peer trading is a next-generation energy management technique that economically benefits proactive consumers (prosumers) transacting their energy as goods and services. At the same time, peer-to-peer energy trading is also expected to help the grid by reducing peak demand, lowering reserve requirements, and curtailing network loss. However, large-scale deployment of peer-to-peer trading in electricity networks poses a number of challenges in modeling transactions in both the virtual and physical layers of the network. As such, this article provides a comprehensive review of the state-of-the-art in research on peer-to-peer energy trading techniques. By doing so, we provide an overview of the key features of peer-to-peer trading and its benefits of relevance to the grid and prosumers. Then, we systematically classify the existing research in terms of the challenges that the studies address in the virtual and the physical layers. We then further identify and discuss those technical approaches that have been extensively used to address the challenges in peer-to-peer transactions. Finally, the paper is concluded with potential future research directions.

S. Verma, Shawn Lu, P. Kenis

Zibin Zheng, Yatao Yang, Xiangdong Niu et al.

Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, the existing methods have poor detection accuracy of electricity theft since most of them were conducted on one-dimensional (1-D) electricity consumption data and failed to capture the periodicity of electricity consumption. In this paper, we originally propose a novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model to address the above concerns. In particular, wide and deep CNN model consists of two components: the wide component and the deep CNN component. The deep CNN component can accurately identify the nonperiodicity of electricity theft and the periodicity of normal electricity usage based on 2-D electricity consumption data. Meanwhile, the wide component can capture the global features of 1-D electricity consumption data. As a result, wide and deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that wide and deep CNN model outperforms other existing methods.

Jiandong Chen, M. Gao, Shulei Cheng et al.

As fundamental data, gross domestic product (GDP) and electricity consumption can be used to effectively evaluate economic status and living standards of residents. Some scholars have estimated gridded GDP and electricity consumption. However, such gridded data have shortcomings, including overestimating real GDP growth, ignoring the heterogeneity of the spatiotemporal dynamics of the grid, and limited time-span. Simultaneously, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer (NPP/VIIRS) nighttime light data, adopted in these studies as a proxy tool, still facing shortcomings, such as imperfect matching results, discontinuity in temporal and spatial changes. In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1 km × 1 km gridded nighttime light data during 1992–2019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1 km × 1 km gridded revised real GDP and electricity consumption during 1992–2019 based on our calibrated nighttime light data. Measurement(s) GDP • electricty consumption Technology Type(s) machine learning

D. Bogdanov, J. Farfán, Kristina Sadovskaia et al.

A transition towards long-term sustainability in global energy systems based on renewable energy resources can mitigate several growing threats to human society simultaneously: greenhouse gas emissions, human-induced climate deviations, and the exceeding of critical planetary boundaries. However, the optimal structure of future systems and potential transition pathways are still open questions. This research describes a global, 100% renewable electricity system, which can be achieved by 2050, and the steps required to enable a realistic transition that prevents societal disruption. Modelling results show that a carbon neutral electricity system can be built in all regions of the world in an economically feasible manner. This radical transformation will require steady but evolutionary changes for the next 35 years, and will lead to sustainable and affordable power supply globally. The technical and economic viability of renewable energy (RE) based energy system is understudied. Here the authors utilized a LUT Energy System Transition Model to indicate that a carbon neutral electricity system can be built in all global regions in an economically feasible way but requires evolutionary changes for the following 35 years.

Jacqueline A. Dowling, Katherine Z. Rinaldi, Tyler H. Ruggles et al.

Summary Reliable and affordable electricity systems based on variable energy sources, such as wind and solar may depend on the ability to store large quantities of low-cost energy over long timescales. Here, we use 39 years of hourly U.S. weather data, and a macro-scale energy model to evaluate capacities and dispatch in least cost, 100% reliable electricity systems with wind and solar generation supported by long-duration storage (LDS; 10 h or greater) and battery storage. We find that the introduction of LDS lowers total system costs relative to wind-solar-battery systems, and that system costs are twice as sensitive to reductions in LDS costs as to reductions in battery costs. In least-cost systems, batteries are used primarily for intra-day storage and LDS is used primarily for inter-season and multi-year storage. Moreover, dependence on LDS increases when the system is optimized over more years. LDS technologies could improve the affordability of renewable electricity.

Zhenyu Zhuo, Ershun Du, Ning Zhang et al.

The Chinese government has set long-term carbon neutrality and renewable energy (RE) development goals for the power sector. Despite a precipitous decline in the costs of RE technologies, the external costs of renewable intermittency and the massive investments in new RE capacities would increase electricity costs. Here, we develop a power system expansion model to comprehensively evaluate changes in the electricity supply costs over a 30-year transition to carbon neutrality. RE supply curves, operating security constraints, and the characteristics of various generation units are modelled in detail to assess the cost variations accurately. According to our results, approximately 5.8 TW of wind and solar photovoltaic capacity would be required to achieve carbon neutrality in the power system by 2050. The electricity supply costs would increase by 9.6 CNY¢/kWh. The major cost shift would result from the substantial investments in RE capacities, flexible generation resources, and network expansion. This study indicates that approximately 5.8 TW of wind and solar photovoltaic capacity would be required to achieve carbon neutrality in China’s power system by 2050. The electricity supply costs would increase by 19.9% or 9.6 CNY¢/kWh.

Hualiang Lv, Zhihong Yang, Bo Liu et al.

Developing an ultimate electromagnetic (EM)-absorbing material that can not only dissipate EM energy but also convert the generated heat into electricity is highly desired but remains a significant challenge. Here, we report a hybrid Sn@C composite with a biological cell-like splitting ability to address this challenge. The composite consisting of Sn nanoparticles embedded within porous carbon would split under a cycled annealing treatment, leading to more dispersed nanoparticles with an ultrasmall size. Benefiting from an electron-transmitting but a phonon-blocking structure created by the splitting behavior, an EM wave-electricity device constructed by the optimum Sn@C composite could achieve an efficiency of EM to heat at widely used frequency region and a maximum thermoelectric figure of merit of 0.62 at 473 K, as well as a constant output voltage and power under the condition of microwave radiation. This work provides a promising solution for solving EM interference with self-powered EM devices. Materials that can harvest electromagnetic (EM) waves and harness the resulting energy would have many applications. Here, the authors present a hybrid composite that produces thermoelectricity from the heating in the EM absorption under microwave radiation.

J. Bedi, Durga Toshniwal

Abstract The increasing world population and availability of energy hungry smart devices are major reasons for alarmingly high electricity consumption in the current times. So far, various simulation tools, engineering and Artificial Intelligence based methods are being used to perform optimal electricity demand forecasting. While engineering methods use dynamic equations to forecast, the AI-based methods use historical data to predict future demand. However, modeling of nonlinear electricity demand patterns is still underdeveloped for robust solutions as the existing methods are useful only for handling short-term dependencies. Moreover, the existing methods are static in nature because they are purely historical data driven. In this paper, we propose a deep learning based framework to forecast electricity demand by taking care of long-term historical dependencies. Initially, the cluster analysis is performed on the electricity consumption data of all months to generate season based segmented data. Subsequently, load trend characterization is carried out to have a deeper insight of metadata falling into each of the clusters. Further, Long Short Term Memory network multi-input multi-output models are trained to forecast electricity demand based upon the season, day and interval data. In the present work, we have also incorporated the concept of moving window based active learning to improve prediction results. To demonstrate the applicability and effectiveness of the proposed approach, it is applied to the electricity consumption data of Union Territory Chandigarh, India. Performance of the proposed approach is evaluated by comparing the prediction results with Artificial Neural Network, Recurrent Neural Network and Support Vector Regression models.

Kechrist Obileke, Helen Onyeaka, E. Meyer et al.

Abstract The unsustainable nature and the environmental impact of fossil fuels have shifted attention to renewable energy and fuel cells, especially in the transportation sector. In this study, the generation of electricity based on the electrons released from biochemical reactions facilitated by microbes is evaluated. Microbial fuel cell (MFC) represents an eco-friendly approach to generating electricity while purifying wastewater concurrently, achieving up to 50% chemical oxygen demand removal and power densities in the range of 420–460 mW/m2. The system utilizes the metabolism power of bacteria for electricity generation. This mini-review is quite comprehensive. It is different from other reviews, it is all-inclusive focusing on the; types of MFCs; substrates and microbes; areas of applications; device performances; design, and technology configuration. All these were evaluated, presented and discussed which can now be accessed in a single paper. It was discovered that higher power density and coulombic efficiency could be achieved through proper selection of microbes, mode of operation, a suitable material for construction, and improved MFC types. Also, the full-scale application of MFC is impeded by materials cost and the wastewater low buffering capacity. Though the electricity generated is still at the demonstration stage, to date, there is no industrial application. Therefore, this study reviewed articles on the technology to set new and insightful perspectives for further research and highlighted steps for scale-up while reinforcing the criteria for microbe selection and their corresponding activity.

Min Xia, Haidong Shao, Xiandong Ma et al.