On the potential of vehicle-to-grid and second-life batteries to provide energy and material security

The global energy transition relies increasingly on lithium-ion batteries for electric transportation and renewable energy integration. Given the highly concentrated supply chain of battery materials, importing regions have a strategic imperative to reduce their reliance on battery material imports through, e.g., battery recycling or reuse. We investigate the potential of vehicle-to-grid and second-life batteries to reduce resource use by displacing new stationary batteries dedicated to grid storage. Based on dynamic material flow analysis, we show that equipping around 50% of electric vehicles with vehicle-to-grid or reusing 40% of electric vehicle batteries for second life each have the potential to fully cover the European Union's need for stationary storage by 2040. This could reduce total primary material demand from 2020-2050 by up to 7.5% and 1.5%, respectively, which could ease geopolitical risks and increase the European Union's energy and material security. Any surplus capacity could be used as a strategic reserve to increase resilience in the face of emergencies such as blackouts or adverse geo-political events.

Transient dataset of household appliances with Intensive switching events

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China.

The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece

The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format.

Reshaping the energy landscape of Crete through renewable energy valleys

Renewable energy valleys (REVs) represent a transformative concept poised to reshape global energy landscapes. These comprehensive ecosystems transition regions from conventional energy sources to sustainable, self-reliant hubs for renewable energy generation, distribution, and consumption. At their core, REVs integrate advanced information and communication technology (ICT), interoperable digital solutions, social innovation processes, and economically viable business models. They offer a vision of decentralized, low-carbon landscapes accessible to all, capable of meeting local energy demands year-round by harnessing multiple renewable energy sources (RES) and leveraging energy storage technologies. This paper provides an overview of the key components and objectives of REVs, including digital integration through advanced ICT technologies and open digital solutions that enable the seamless management of RES within the REV. The social innovation aspect via the REV's active communities is also examined, encouraging their participation in the co-design, implementation, and benefit-sharing of renewable energy solutions. In addition, business viability through sustainable business models central to the REV framework is proposed, ensuring affordability and accessibility to all stakeholders. The paper presents a case study of Crete, showcasing how the REV idea can work in real life. Crete utilizes various energy sources to become energy-independent, lower carbon emissions, and enhance system resilience. Advanced energy storage technologies are employed to ensure supply and demand balance within the REV. Situated on the picturesque island of Crete, Greece, it is pioneering the establishment of a Renewable Energy Valley 'Living Lab' (REV-Lab), integrating Community Energy Labs (CELs) as innovation hubs. This initiative exemplifies the REV model, striving to create a digitalized, distributed, and low-carbon landscape accessible to all residents throughout the year.

A dataset for multi-faceted analysis of electric vehicle charging transactions

This study discloses a dataset of electric vehicles' (EVs') charging transactions at a scale for multi-faceted analysis from both EV charger and user perspectives. The data comprises whole sessions that occurred during a charging operation company's annual commercial operation period, specifically including identifiers and charger location categories. For data acquisition, machine-to-machine wireless communication system with proper retransmission for interruption is utilised. The entire dataset is newly collected and is available with 72,856 sessions from 2,337 EV users and 2,119 chargers. The dataset can be used in a variety of ways for the functioning of power systems and markets, including EV charging service businesses, charger installation siting, demand transaction market design, and long-term investment planning of EV-related infrastructure.

Review of Building Integrated Photovoltaics System for Electric Vehicle Charging

The transition to sustainable transportation has fueled the need for innovative electric vehicle (EV) charging solutions. Building Integrated Photovoltaics (BIPV) systems have emerged as a promising technology that combines renewable energy generation with the infra-structure of buildings. This paper comprehensively reviews the BIPV system for EV charging, focusing on its technology, application, and performance. The review identifies the gaps in the existing literature, emphasizing the need for a thorough examination of BIPV systems in the context of EV charging. A detailed review of BIPV technology and its application in EV charging is presented, covering aspects such as the generation of solar cell technology, BIPV system installation, design options and influencing factors. Furthermore, the review examines the performance of BIPV systems for EV charging, focusing on energy, economic, and environmental parameters and their comparison with previous studies. Additionally, the paper explores current trends in energy management for BIPV and EV charging, highlighting the need for effective integration and recommending strategies to optimize energy utilization. Combining BIPV with EV charging provides a promising approach to power EV chargers, enhances building energy efficiency, optimizes the building space, reduces energy losses, and decreases grid dependence. Utilizing BIPV-generated electricity for EV charging provides electricity and fuel savings, offers financial incentives, and increases the market value of the building infrastructure. It significantly lowers greenhouse gas emissions associated with grid and vehicle emissions. It creates a closed-loop circular economic system where energy is produced, consumed, and stored within the building. The paper underscores the importance of effective integration between Building Integrated Photovoltaics (BIPV) and Electric Vehicle (EV) charging, emphasizing the necessity of innovative grid technologies, energy storage solutions, and demand-response energy management strategies to overcome diverse challenges. Overall, the study contributes to the knowledge of BIPV systems for EV charging by presenting practical energy management, effectiveness and sustainability implications. It serves as a valuable resource for researchers, practitioners, and policymakers working towards sustainable transportation and energy systems.

Wind turbine database for intelligent operation and maintenance strategies

With the aim of helping researchers to develop intelligent operation and maintenance strategies, in this manuscript, an extensive 3-years Supervisory Control and Data Acquisition database of five Fuhrländer FL2500 2.5 MW wind turbines is presented. The database contains 312 analogous variables recorded at 5-minute intervals, from 78 different sensors. The reported values for each sensor are minimum, maximum, mean, and standard deviation. The database also contains the alarm events, indicating the system and subsystem and a small description. Finally, a set of functions to download specific subsets of the whole database is freely available in Matlab, R, and Python. To demonstrate the usefulness of this database, an illustrative example is given. In this example, different gearbox variables are selected to estimate a target variable to detect whether or not the estimate differs from the actual value provided for the sensor. By using this normality modelling approach, it is possible to detect rotor malfunction when the estimate differs from the actual measured value.

Interfacial Solar Evaporation: From Fundamental Research to Applications

In the last decade, interfacial solar steam generation (ISSG), powered by natural sunlight garnered significant attention due to its great potential for low-cost and environmentally friendly clean water production in alignment with the global decarbonization efforts. This review aims to share the knowledge and engage with a broader readership about the current progress of ISSG technology and the facing challenges to promote further advancements toward practical applications. The first part of this review assesses the current strategies for enhancing the energy efficiency of ISSG systems, including optimizing light absorption, reducing energy losses, harvesting additional energy, and lowering evaporation enthalpy. Subsequently, the current challenges faced by ISSG technologies, notably salt accumulation and bio-fouling issues in practical applications, are elucidated and contemporary methods are discussed to overcome these challenges. In the end, potential applications of ISSG, ranging from initial seawater desalination and industrial wastewater purification to power generation, sterilization, soil remediation, and innovative concept of solar sea farm, are introduced, highlighting the promising potential of ISSG technology in contributing to sustainable and environmentally conscious practices. Based on the review and in-depth understanding of these aspects, the future research focuses are proposed to address potential issues in both fundamental research and practical applications.

Versatile Mesoporous All-Wood Sponge Enabled by In Situ Fibrillation toward Indoor-Outdoor Energy Management and Conversion

The on-demand regulation of cell wall microstructures is crucial for developing wood as a functional building material for energy management and conversion. Here, a novel strategy based on reactive deep eutectic solvent is developed to one-step in situ fibrillate wood via disrupting the hydrogen bonding networks in cell walls and simultaneously carboxylating wood components, without significantly altering the native hierarchical structures of wood. Benefiting from its distinctive cell wall structure composed of individualized yet well-organized lignocellulose nanofibrils, in situ fibrillated wood exhibits a prominent mesoporous structure with a specific surface area of 81 m2/g. It represents a robust sponge material (5 MPa at 80% strain) with excellent durability. Due to the enhanced compressibility and charge polarization capacity, the in situ fibrillated wood (10 × 11 × 12 mm3) can generate a piezoelectric output voltage of up to 2 V under 221 kPa stress. The favorable microstructural characteristics render in situ fibrillated wood with highly thermal-insulating properties, high solar reflectivity, and mid-infrared emissivity, favoring outdoor passive cooling effects with a subambient temperature drop of 6 °C. Combining its controllable, durable, and eco-friendly attributes, our developed wood sponge represents a versatile structural material suitable for indoor/outdoor energy-saving applications.

City-level building operation and end-use carbon emissions dataset from China for 2015-2020

The building sector, which accounts for over 20% of China's total energy-related carbon emissions, has great potential to reduce emissions and is critical to achieving China's emissions peak and carbon neutrality targets. However, the lack of data on operational carbon emissions and end-use carbon emissions in the building sector at the city level has become a major barrier to the development of building energy conservation policies and carbon peaking action plans. This study uses a combination of "top-down" and "bottom-up" methods to account for the operational carbon emissions of buildings in 321 cities in China from 2015 to 2020. The energy consumption in buildings is further broken down into six end uses: central heating, distributed heating, cooking and water heating (C&W), lighting, cooling, appliances and others (A&O). The dataset can serve as a reference to support city-level policies on peak building emissions and is of great value for the improvement of the carbon emissions statistical accounting system.

Occupant behavior, thermal environment, and appliance electricity use of a single-family apartment in China

The household is the basic unit of a residential community or building. High-resolution, long-term open data are necessary to help study residential electricity consumption, smart home technologies, and electricity flexibility technologies at household level. This paper introduces an IoT-based data collection platform (IDCP) consisting of gateways, sensors, and cloud servers. This platform can collect data on the occupant presence, indoor environment, window-opening states, and appliance electricity consumption simultaneously. This study deployed the IDCP in a single-family apartment in Beijing, China, and compiled a dataset, namely, CN-OBEE, including data on the per-minute occupant behavior, thermal environment, and appliance electricity use of the apartment for an entire year (from May 31, 2021, to May 31, 2022), and hourly meteorological data collected by the nearest national weather station during the same period. This dataset is the first detailed and publicly available occupant behavior and electricity use dataset for Chinese homes. As a regional feature, the dataset compiled by this study includes window-opening behavior and the use of split air conditioners (ACs).

A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles

Flexibility has become increasingly important considering the intermittency of variable renewable energy in low-carbon energy systems. Electrified transportation exhibits great potential to provide flexibility. This article analyzed and compared the flexibility values of battery electric vehicles and fuel cell electric vehicles for planning and operating interdependent electricity and hydrogen supply chains while considering battery degradation costs. A cross-scale framework involving both macro-level and micro-level models was proposed to compute the profits of flexible EV refueling/charging with battery degradation considered. Here we show that the flexibility reduction after considering battery degradation is quantified by at least 4.7% of the minimum system cost and enlarged under fast charging and low-temperature scenarios. Our findings imply that energy policies and relevant management technologies are crucial to shaping the comparative flexibility advantage of the two transportation electrification pathways. The proposed cross-scale methodology has broad implications for the assessment of emerging energy technologies with complex dynamics.

A resilient battery electric bus transit system configuration

Electric mobility is fundamental to combat climate change and attaining the United Nations Sustainable Development Goals (SDG-11). However, electric mobility necessitates a seamless integration between power and transportation systems, as the resiliency of both systems is becoming far more interdependent. Here, we focus on disruption to Battery Electric Bus (BEB) transit system charging infrastructure and offer a resilient BEB transit system planning model. The proposed model optimizes the BEB system costs while ensuring the system's robustness against simultaneous charging station failures. In our case study, a single charging station failure would lead to up to 34.03% service reduction, and two simultaneous failures would reduce the service by up to 58.18%. Our proposed two-stage robust model addresses this issue with a relatively small added cost (3.26% and 8.12% higher than the base model). This cost enables uninterrupted BEB system operation during disruption, ensuring personal mobility, social interaction, and economic productivity.

High-resolution electric power load data of an industrial park with multiple types of buildings in China

Considering the growing demand for electricity in industrial parks, understanding their electric power load patterns is critical for improving energy efficiency and ensuring the rational utilization of energy resources. However, the detailed electric power load data of various buildings in industrial parks are rarely available and accessible, which hinders the related studies. In this context, we present the electric power load data of 6 years (from January 1, 2016 to December 31, 2021) for various types of buildings in an industrial park in Suzhou, China. The data are obtained from smart meters and have various time resolutions (i.e., 5 minutes, 30 minutes, and 1 hour). This work describes the data collection, processing process, and different imputation methods. The high-resolution electric power load data can be used for various research tasks, including load prediction, load pattern recognition, anomaly detection, and demand response strategy development.

Pickering Emulsion Templated 3D Cylindrical Open Porous Aerogel for Highly Efficient Solar Steam Generation

Porous-structured evaporators have been fabricated for achieving a high clean water throughput due to their maximized surface area. However, most of the evaporation surfaces in the porous structure are not active because of the trapped vapor in pores. Herein, a three-dimensional (3D) cylindrical aerogel-based photothermal evaporator with a disordered interconnected hierarchical porous structure is developed via a Pickering emulsion-involved polymerization method. The obtained cotton cellulose/aramid nanofibers/polypyrrole (CAP) aerogel-based evaporator achieved all-cold evaporation under 1.0 sun irradiation, which not only completely eliminated energy loss via radiation, convection, and conduction, but also harvested massive extra energy from the surrounding environment and bulk water, thus significantly increasing the total energy input for vapor generation to deliver an extremely high evaporation rate of 5.368 kg m-2 h-1 . In addition, with the external convective flow, solar steam generation over the evaporator can be dramatically enhanced due to fast vapor diffusion out of its unique opened porous structure, realizing an ultrahigh evaporation rate of 18.539 kg m-2 h-1 under 1.0 sun and 4.0 m s-1 . Moreover, this evaporator can continuously operate with concentrated salt solution (20 wt.% NaCl). This work advances rational design and construction of solar evaporator to promote the application of solar evaporation technology in freshwater production.

A Compact RF Energy Harvesting Wireless Sensor Node with an Energy Intensity Adaptive Management Algorithm

This paper presents a compact RF energy harvesting wireless sensor node with the antenna, rectifier, energy management circuits, and load integrated on a single printed circuit board and a total size of 53 mm × 59.77 mm × 4.5 mm. By etching rectangular slots in the radiation patch, the antenna area is reduced by 13.9%. The antenna is tested to have an S11 of -24.9 dB at 2.437 GHz and a maximum gain of 4.8 dBi. The rectifier has a maximum RF-to-DC conversion efficiency of 52.53% at 7 dBm input energy. The proposed WSN can achieve self-powered operation at a distance of 13.4 m from the transmitter source. To enhance the conversion efficiency under different input energy densities, this paper establishes an energy model for two operating modes and proposes an energy-intensity adaptive management algorithm. The experiments demonstrated that the proposed WSN can effectively distinguish between the two operating modes based on input energy intensity and realize efficient energy management.

A Compact Stacked RF Energy Harvester with Multi-Condition Adaptive Energy Management Circuits

This paper presents a compact stacked RF energy harvester operating in the WiFi band with multi-condition adaptive energy management circuits (MCA-EMCs). The harvester is divided into antennas, impedance matching networks, rectifiers, and MCA-EMCs. The antenna is based on a polytetrafluoroethylene (PTFE) substrate using the microstrip antenna structure and a ring slot in the ground plane to reduce the antenna area by 13.7%. The rectifier, impedance matching network, and MCA-EMC are made on a single FR4 substrate. The rectifier has a maximum conversion efficiency of 33.8% at 5 dBm input. The MCA-EMC has two operating modes to adapt to multiple operating conditions, in which Mode 1 outputs 1.5 V and has a higher energy conversion efficiency of up to 93.56%, and Mode 2 supports a minimum starting input voltage of 0.33 V and multiple output voltages of 2.85-2.45 V and 1.5 V. The proposed RF energy harvester is integrated by multiple-layer stacking with a total size of 53 mm × 43.5 mm × 5.9 mm. The test results show that the proposed RF energy harvester can drive a wall clock (30 cm in diameter) at 10 cm distance and a hygrometer at 122 cm distance with a home router as the transmitting source.

Enhancing psychological well-being of school teachers in India: role of energy management, thriving, and stress

Purpose

The psychological well-being of school teachers is a growing concern in the post-pandemic era. Many initiatives are undertaken by individual school authorities and government agencies to address this issue. In this study, we examine the impact of energy management, thriving, and stress on the psychological well-being of school teachers in India.

Method

Data was collected from 356 school teachers in Rajasthan, India, through a cross-sectional questionnaire survey. These teachers were working both in rural and urban areas. The relationship among energy management, thriving, stress, and psychological well-being was analyzed using hierarchical regression.

Results

The findings suggest that energy management positively impacts psychological well-being. A mediating effect of thriving and stress on the relationship between energy management and psychological well-being was also found. The results support that psychological well-being can be enhanced by investing in managing school teachers' energy levels.

Discussion

These results contribute to our theoretical and practical understanding of factors that can enhance the psychological well-being of school teachers and improve the quality of education. Institutes may design and implement interventions on energy management to enhance the psychological well-being of school teachers.

BSEMS-A Blockchain-Based Smart Energy Measurement System

The modern world's increasing reliance on automated systems for everyday tasks has resulted in a corresponding rise in power consumption. The demand is further augmented by increased sales of electric vehicles, smart cities, smart transportation, etc. This growing dependence underscores the critical necessity for a robust smart energy measurement and management system to ensure a continuous and efficient power supply. However, implementing such a system presents a set of challenges, particularly concerning the transparency, security, and trustworthiness of data storage and retrieval. Blockchain technology offers an innovative solution in the form of a distributed ledger, which guarantees secure and transparent transaction storage and retrieval. This research introduces a blockchain-based system, utilising Hyperledger Fabric and smart contracts, designed for the secure storage and retrieval of consumers' energy consumption data. Finally, a user-friendly web portal was designed and developed using the node.js framework, offering an accessible and intuitive interface to monitor and manage energy consumption effectively.

Maximize Lifetime of Wireless Rechargeable Sensor Networks with Mobile Energy-Limited Charging Device

Mobile charging devices (MCDs) have been regarded as a promising way to solve the energy shortage of wireless sensor networks. Due to ignoring some important factors, such as redundant sensor nodes, there is still room to improve network lifetimes. We propose a charging strategy for wireless sensor networks with one energy-limited MCD. To give the best support for sensor nodes which need charging the most, an algorithm is proposed to find the minimum sensor nodes which keep the coverage and connectivity of the network and have the least energy requirements. Then, the goal of maximizing network lifetime is changed into how to utilize the limited energy of the MCD to guarantee the minimum sensor nodes work as long as possible. If the MCD has enough energy for all sensor nodes, the charging algorithm is designed to minimize the outage time of the network and maximize charging efficiency. Otherwise, if the energy capacity is larger than the least energy requirement, the charging target minimizes the outage time of the minimum sensor node; otherwise the charging problem becomes maximizing the lifetime of minimum sensor nodes, which has lower complexity. The results of simulation experiments confirm that our scheme prolongs network lifetime and improves charging efficiency.

Global land and water limits to electrolytic hydrogen production using wind and solar resources

Proposals for achieving net-zero emissions by 2050 include scaling-up electrolytic hydrogen production, however, this poses technical, economic, and environmental challenges. One such challenge is for policymakers to ensure a sustainable future for the environment including freshwater and land resources while facilitating low-carbon hydrogen production using renewable wind and solar energy. We establish a country-by-country reference scenario for hydrogen demand in 2050 and compare it with land and water availability. Our analysis highlights countries that will be constrained by domestic natural resources to achieve electrolytic hydrogen self-sufficiency in a net-zero target. Depending on land allocation for the installation of solar panels or wind turbines, less than 50% of hydrogen demand in 2050 could be met through a local production without land or water scarcity. Our findings identify potential importers and exporters of hydrogen or, conversely, exporters or importers of industries that would rely on electrolytic hydrogen. The abundance of land and water resources in Southern and Central-East Africa, West Africa, South America, Canada, and Australia make these countries potential leaders in hydrogen export.

Design and analysis of a passive exoskeleton with its hip joint energy-storage

A novel passive hip exoskeleton has been designed and built with the aim of reducing metabolic consumption during walking by a passive way of storing the negative mechanical energy in the deceleration phase and releasing it in the acceleration phase. A ratchet spiral spring mechanism with a set of double stable switches is designed inside the exoskeleton for the above purpose. An analysis is conducted on the mechanism and the switching timing for the energy management to automatically store or release the energy according to the biomechanics of walking. In addition, a gravity-balance mechanism embedded inside the exoskeleton is designed as well to minimize the influence of the lower limb weight on muscle work. Human-exoskeleton interaction has been studied using the Opensim software, and simulation results demonstrated the effectiveness of the exoskeleton in reducing metabolic consumption during walking. An exoskeleton prototype has been built and tested with experiments measuring assistive torque and surface electromyography signal, confirming the effectiveness of the gravity-balance mechanism and energy-storage method, as well as the exoskeleton's actual assistive effect.

A Deep Learning-Driven Self-Conscious Distributed Cyber-Physical System for Renewable Energy Communities

The Internet of Things (IoT) is transforming various domains, including smart energy management, by enabling the integration of complex digital and physical components in distributed cyber-physical systems (DCPSs). The design of DCPSs has so far been focused on performance-related, non-functional requirements. However, with the growing power consumption and computation expenses, sustainability is becoming an important aspect to consider. This has led to the concept of energy-aware DCPSs, which integrate conventional non-functional requirements with additional attributes for sustainability, such as energy consumption. This research activity aimed to investigate and develop energy-aware architectural models and edge/cloud computing technologies to design next-generation, AI-enabled (and, specifically, deep-learning-enhanced), self-conscious IoT-extended DCPSs. Our key contributions include energy-aware edge-to-cloud architectural models and technologies, the orchestration of a (possibly federated) edge-to-cloud infrastructure, abstractions and unified models for distributed heterogeneous virtualized resources, innovative machine learning algorithms for the dynamic reallocation and reconfiguration of energy resources, and the management of energy communities. The proposed solution was validated through case studies on optimizing renewable energy communities (RECs), or energy-aware DCPSs, which are particularly challenging due to their unique requirements and constraints; in more detail, in this work, we aim to define the optimal implementation of an energy-aware DCPS. Moreover, smart grids play a crucial role in developing energy-aware DCPSs, providing a flexible and efficient power system integrating renewable energy sources, microgrids, and other distributed energy resources. The proposed energy-aware DCPSs contribute to the development of smart grids by providing a sustainable, self-consistent, and efficient way to manage energy distribution and consumption. The performance demonstrates our approach's effectiveness for consumption and production (based on RMSE and MAE metrics). Our research supports the transition towards a more sustainable future, where communities adopting REC principles become key players in the energy landscape.

Malpighian tubules of Rhodnius prolixus: More than post-prandial diuresis

Rhodnius prolixus, a major vector of Chagas disease, may be considered the model upon which the foundations of insect physiology and biochemistry were built. It is an obligate blood feeder in which the blood meal triggers growth, development and reproduction. The blood meal also triggers a post-prandial diuresis to maintain osmotic homeostasis. In R. prolixus, as with other insects, the Malpighian tubules play a critical role in this diuresis, and much has been learned about diuresis in R. prolixus, and in other model insects. But the post-genomic era has brought new insights, identifying functions quite apart from diuresis for Malpighian tubules. Indeed, microarrays, transcriptomes, and proteomics have revealed the major roles that Malpighian tubules play in immunity, detoxification, pesticide resistance, and in tolerance to overall stress. This is particularly relevant to R. prolixus since gorging on blood creates several challenges in addition to osmotic balance. Xenobiotics may be present in the blood or toxins may be produced by metabolism of blood; and these must be neutralized and excreted. These processes have not been well described at the molecular level for Malpighian tubules of R. prolixus. This paper will review the involvement of Malpighian tubules in immunity and detoxification, identifying new aspects for Malpighian tubule physiology of R. prolixus by virtue of a transcriptome analysis. The transcriptome analysis indicates the potential of Malpighian tubules of R. prolixus to mount a robust innate immune response, and to contribute to antioxidant production and heme detoxification.

PLEIAData: consumption, HVAC, temperature, weather and motion sensor data for smart buildings applications

The current cost that energy represents is crucial in a field like climate control which has high energy demands, therefore its reduction must be prioritized. The expansion of ICT and IoT come with an extensive deployment of sensors and computation infrastructure creating an opportunity to analyze and optimize energy management. Data on building internal and external conditions is essential for developing efficient control strategies in order to minimize energy consumption while maintaining users' comfort inside. We here present a dataset that provides key features that could be useful for a wide range of applications in the context of modeling temperature and consumption via Artificial Intelligence algorithms. The data gathering has taken place for almost 1 year in the Pleiades building of the University of Murcia, which is a pilot building of the European project PHOENIX aiming to improve building energy efficiency.

Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System

Improvements in modelling energy systems of populous emerging economies are highly decisive for a successful global energy transition. The models used-increasingly open source-still need more appropriate open data. As an illustrative example, we take the Brazilian energy system, which has great potential for renewable energy resources but still relies heavily on fossil fuels. We provide a comprehensive open dataset for scenario analyses, which can be directly used with the popular open energy system model PyPSA and other modelling frameworks. It includes three categories: (1) time series data of variable renewable potentials, electricity load profiles, inflows for the hydropower plants, and cross-border electricity exchanges; (2) geospatial data on the administrative division of the Brazilian federal states; (3) tabular data, which contains power plant data with installed and planned generation capacities, aggregated grid network topology, biomass thermal plant potential, as well as scenarios of energy demand. Our dataset could enable further global or country-specific energy system studies based on open data relevant to decarbonizing Brazil's energy system.

Self-Powered Synchronized Switching Interface Circuit for Piezoelectric Footstep Energy Harvesting

Piezoelectric Vibration converters are nowadays gaining importance for supplying low-powered sensor nodes and wearable electronic devices. Energy management interfaces are thereby needed to ensure voltage compatibility between the harvester element and the electric load. To improve power extraction ability, resonant interfaces such as Parallel Synchronized Switch Harvesting on Inductor (P-SSHI) have been proposed. The main challenges for designing this type of energy management circuits are to realise self-powered solutions and increase the energy efficiency and adaptability of the interface for low-power operation modes corresponding to low frequencies and irregular vibration mechanical energy sources. In this work, a novel Self-Powered (SP P-SSHI) energy management circuit is proposed which is able to harvest energy from piezoelectric converters at low frequencies and irregular chock like footstep input excitations. It has a good power extraction ability and is adaptable for different storage capacitors and loads. As a proof of concept, a piezoelectric shoe insole with six integrated parallel piezoelectric sensors (PEts) was designed and implemented to validate the performance of the energy management interface circuit. Under a vibration excitation of 1 Hz corresponding to a (moderate walking speed), the maximum reached efficiency and power of the proposed interface is 83.02% and 3.6 mW respectively for the designed insole, a 10 kΩ resistive load and a 10 μF storage capacitor. The enhanced SP-PSSHI circuit was validated to charge a 10 μF capacitor to 6 V in 3.94 s and a 1 mF capacitor to 3.2 V in 27.64 s. The proposed energy management interface has a cold start-up ability and was also validated to charge a (65 mAh, 3.1 V) maganese dioxide coin cell Lithium battery (ML 2032), demonstrating the ability of the proposed wearable piezoelectric energy harvesting system to provide an autonomous power supply for wearable wireless sensors.

An innovative transactive energy architecture for community microgrids in modern multi-carrier energy networks: a Chicago case study

As the technology of multi-energy carbon-free systems is strikingly developed, renewable-based multi-vector energy integration has become a prevalent trend in the decarbonization procedure of multi-carrier energy networks (MCENs). This paper proposes a fair transactive energy model for structuring an innovative local multi-energy trading market to allow multi-carrier multi-microgrids (MCMGs) with 100% renewable energy sources (RESs) in Chicago for free energy exchange aiming to balance energy in the renewable-dominant environment. Indeed, the main goal of the proposed model is to facilitate the modernization of future MCENs that are targeted to be equipped with 100% RESs and require a holistic model engaged with innovative technologies for the realization. To this end, the transactive energy architecture is designed for techno-environmental-economic assessing hybrid MCMGs to increase their flexibility in unbroken energy serving, decreasing their dependency on the main grid, and improving their economic benefits by considering their contribution level in energy interactions. To effectively model uncertainties of MCENs with 100% RESs, the novel hybrid technique is proposed that considers various stochastic changes of uncertain parameters to achieve confident results. The results highlighted the capability of the proposed model in effectively utilizing fully produced clean energy as well as continuously multi-energy serving of MCMGs in the presence of 100% RESs. Moreover, MCMGs reached techno-environmental-economic benefits by operating under the proposed transactive energy-based model, in which the technical, environmental, and economic goals are respectively realized by considering all constraints of MCENs, producing 100% clean energy by RESs, and reducing the total energy cost from $1,274,742.55 in the based model to $1,159,235.89 in the proposed one.

Techno-economic feasibility analysis of an extreme heat flux micro-cooler

An estimated 70% of the electricity in the United States currently passes through power conversion electronics, and this percentage is projected to increase eventually to up to 100%. At a global scale, wide adoption of highly efficient power electronics technologies is thus anticipated to have a major impact on worldwide energy consumption. As described in this perspective, for power conversion, outstanding thermal management for semiconductor devices is one key to unlocking this potentially massive energy savings. Integrated microscale cooling has been positively identified for such thermal management of future high-heat-flux, i.e., 1 kW/cm², wide-bandgap (WBG) semiconductor devices. In this work, we connect this advanced cooling approach to the energy impact of using WBG devices and further present a techno-economic analysis to clarify the projected status of performance, manufacturing approaches, fabrication costs, and remaining barriers to the adoption of such cooling technology.

Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home

This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012-2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case.

Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage

This paper highlights a robust optimization and power management algorithm that supervises the energy transfer flow to meet the photovoltaic (PV) electric vehicle demand, even when the traction system is in motion. The power stage of the studied system consists of a triple-junction PV generator as the main energy source, a lithium-ion battery as an auxiliary energy source, and an electric vehicle. The input-output signal adaptation is made by using a stage of energy conversion. A bidirectional DC-DC buck-boost connects the battery to the DC-link. Two unidirectional boost converters interface between the PV generator and the DC link. One is controlled with a maximum power point tracking (MPPT) algorithm to reach the maximum power points. The other is used to control the voltage across the DC-link. The converters are connected to the electric vehicle via a three-phase inverter via the same DC-link. By considering the nonlinear behavior of these elements, dynamic models are developed. A robust nonlinear MPPT algorithm has been developed owing to the nonlinear dynamics of the PV generator, metrological condition variations, and load changes. The high performance of the MPPT algorithm is effectively highlighted over a comparative study with two classical P & O and the fuzzy logic MPPT algorithms. A nonlinear control based on the Lyapunov function has been developed to simultaneously regulate the DC-link voltage and control battery charging and discharging operations. An energy management rule-based strategy is presented to effectively supervise the power flow. The conceived system, energy management, and control algorithms are implemented and verified in the Matlab/Simulink environment. Obtained results are presented and discussed under different operating conditions.

Thermomechanical Performance Analysis of Novel Cement-Based Building Envelopes with Enhanced Passive Insulation Properties

The design of new insulating envelopes is a direct route towards energy efficient buildings. The combinations of novel materials, such as phase-change (PCM), and advanced manufacturing techniques, such as additive manufacturing, may harness important changes in the designing of building envelopes. In this work we propose a novel methodology for the design of cement-based building envelopes. Namely, we combined the use of a multiscale, multiphysical simulation framework with advanced synthesis techniques, such as the use of phase-change materials and additive manufacturing for the design of concrete envelopes with enhanced insulation properties. At the material scale, microencapsulated PCMs are added to a cementitious matrix to increase heat storage. Next, at the component level, we create novel designs for the blocks, here defined as HEXCEM, by means of additive manufacturing. The material and component design process is strongly supported on heat transfer simulations with the use of the finite element method. Effective thermal properties of the mixes can be obtained and subsequently used in macroscale simulations to account for the effect of the volume fraction of PCMs. From the experimental and numerical tests, we report an increase in the the thermal inertia, which results in thermal comfort indoors.

Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment

Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.

Restoration of Attention by Rest in a Multitasking World: Theory, Methodology, and Empirical Evidence

In this work, we evaluate the status of both theory and empirical evidence in the field of experimental rest-break research based on a framework that combines mental-chronometry and psychometric-measurement theory. To this end, we (1) provide a taxonomy of rest breaks according to which empirical studies can be classified (e.g., by differentiating between long, short, and micro-rest breaks based on context and temporal properties). Then, we (2) evaluate the theorizing in both the basic and applied fields of research and explain how popular concepts (e.g., ego depletion model, opportunity cost theory, attention restoration theory, action readiness, etc.) relate to each other in contemporary theoretical debates. Here, we highlight differences between all these models in the light of two symbolic categories, termed the resource-based and satiation-based model, including aspects related to the dynamics and the control (strategic or non-strategic) mechanisms at work. Based on a critical assessment of existing methodological and theoretical approaches, we finally (3) provide a set of guidelines for both theory building and future empirical approaches to the experimental study of rest breaks. We conclude that a psychometrically advanced and theoretically focused research of rest and recovery has the potential to finally provide a sound scientific basis to eventually mitigate the adverse effects of ever increasing task demands on performance and well-being in a multitasking world at work and leisure.

Characterization System for Heat-Energy to Electric-Energy Conversion from Concrete by Means of a Thermoelectric Module

The present work describes the implementation of a prototype to characterize thermoelectric modules (TEM). The goal is to study the energy conversion by means of thermoelectric modules mounted on concrete structures. The proposed experimental system is used for the electrical characterization of a commercially available thermoelectric module TEC1-12710 to prove its operation while embedded in a concrete slab, typical of building constructions. In this case, the parameters that define thermal energy conversion into electrical energy are open-circuit voltage generation, loaded circuit voltage generation, and load current. A known external load is connected to the terminals of the TEM for the purpose of its electric characterization. An electrical heating element on the hot side and a thermoelectric cooler on the cold side produce a temperature difference on the concrete slab. This arrangement allows the emulation of a temperature gradient produced by sunlight over a concrete structure. The objective is to measure the resulting electrical energy produced by the combination of concrete slab and the thermoelectric module. By controlling the temperature difference between the sides of the thermoelectric module under test, it is possible to simulate the effect of the temperature gradient under different sunlight conditions. Two digital PI controllers regulate the temperature conditions, thus providing controlled conditions for the experiments.

Energy Management Strategy for an Autonomous Hybrid Power Plant Destined to Supply Controllable Loads

This paper proposes an energy management strategy (EMS) for a hybrid stand-alone plant destined to supply controllable loads. The plant is composed of photovoltaic panels (PV), a wind turbine, a diesel generator, and a battery bank. The set of the power sources supplies controllable electrical loads. The proposed EMS aims to ensure the power supply of the loads by providing the required electrical power. Moreover, the EMS ensures the maximum use of the power generated by the renewable sources and therefore minimizes the use of the genset, and it ensures that the batteries bank operates into the prefixed values of state of charge to ensure their safe operation. The EMS provides the switching control of the switches that link the plant components and decides on the loads' operation. The simulation of the system using measured climatic data of Mostoles (Madrid, Spain) shows that the proposed EMS fulfills the designed objectives.

Design and Analysis of In-Pipe Hydro-Turbine for an Optimized Nearly Zero Energy Building

Pakistan receives Direct Normal Irradiation (DNI) exceeding 2000 kWh/m²/annum on approximately 83% of its land, which is very suitable for photovoltaic production. This energy can be easily utilized in conjunction with other renewable energy resources to meet the energy demands and reduce the carbon footprint of the country. In this research, a hybrid renewable energy solution based on a nearly Zero Energy Building (nZEB) model is proposed for a university facility. The building in consideration has a continuous flow of water through its water delivery vertical pipelines. A horizontal-axis spherical helical turbine is designed in SolidWorks and is analyzed through a computational fluid dynamics (CFD) analysis in ANSYS Fluent 18.1 based on the K-epsilon turbulent model. Results obtained from ANSYS Fluent have shown that a 24 feet vertical channel with a water flow of 0.2309 m³/s and velocity of 12.66 m/s can run the designed hydroelectric turbine, delivering 168 W of mechanical power at 250 r.p.m. Based on the turbine, a hybrid renewable energy system (HRES) comprising photovoltaic and hydroelectric power is modelled and analyzed in HOMER Pro software. Among different architectures, it was found that architecture with hydroelectric and photovoltaic energy provided the best COE of $0.09418.

Energy and Environmental Assessment of Steam Management Optimization in an Ethylene Plant

Steam crackers (ethylene plants) belong to the most complex industrial plants and offer significant potential for energy-saving translated into the reduction of greenhouse gas emissions. Steam export to or import from adjacent units or complexes can boost the associated financial benefit, but its energy and environmental impact are questionable. A study was carried out on a medium-capacity ethylene plant using field data to: 1. Estimate the energy savings potential achievable by optimizing internal steam management and optimizing steam export/import; 2. Quantify the associated change in air pollutant emissions; 3. Analyze the impact of the increasing carbon price on the measures adopted. Internal steam management optimization yielded steam let-down rate minimization and resulted in a 5% (87 TJ/year) reduction in steam cracker’s steam boiler fuel consumption and the associated cut of CO₂ emissions by almost 4900 t/year and that of NO_x emissions by more than 5 t/year. Steam import to the ethylene plant from the refinery proved to be purely economic-driven, as it increased the net fuel consumption of the ethylene plant and the refinery complex by 12 TJ/year and resulted in an increase of net emissions of nearly all considered air pollutants (more than 7000 t/year of CO₂, over 15 t/year of NO_x, over 18 t/year of SO_x) except for CO, where the net change was almost zero. The effect of external emissions change due to the associated backpressure electricity production surplus (over 11 GWh/year) was too low to compensate for this increase unless fossil fuel-based electricity production was considered. The increase of carbon price impact on the internal steam management optimization economics was favorable, while a switch to steam export from the ethylene plant, instead of steam import, might be feasible if the carbon price increased to over 100 €/tCO₂.

Inequitable and heterogeneous impacts on electricity consumption from COVID-19 mitigation measures

The COVID-19 pandemic has exacerbated energy insecurity and economic hardship among vulnerable populations. This paper provides robust empirical evidence of the degree to which COVID-19 mitigation measures, especially the mandates of school closure and limiting business operations, have impacted electricity consumption behavior in low-income and ethnic minority groups in the United States. We use a regression discontinuity design applied to individual-consumer-level high-frequency smart meter data in Arizona and Illinois to highlight the disparities in mitigation measure impacts. We find that the mandates of school closures and limiting business operations increase residential electricity consumption by 4-5%, but reduce commercial electricity consumption by 5-8%. Considerable heterogeneity is observed across income and race: low-income and ethnic-minority populations experience a larger electricity consumption increase, reflecting the disproportionate impact of COVID-19 on electricity insecurity in the residential sector. Policies that address energy insecurity, especially during the pandemic, become essentially important.

Heat Balance Calculation and Energy Efficiency Analysis for Building Clusters Based on Psychrometric Chart

How to perform accurate calculation of heat balance and quantitative analysis of energy efficiency for building clusters is an urgent problem to be solved to reduce building energy consumption and improve energy utilization efficiency. This article proposes a method for the heat balance calculation and energy efficiency analysis of building clusters based on enthalpy and humidity diagrams and applies it to the energy management of building clusters containing primary return air systems and heating pipe networks. Firstly, the basic structure and energy management principle of building clusters with a primary return air system and a heating pipe network were given, and the heat balance calculation and energy efficiency analysis method based on i-d diagram was proposed to realize the accurate calculation of heat load and the quantification of energy utilization. Secondly, the energy management model of the building cluster with a primary return air system and a heating pipe network was established to efficiently manage the indoor temperature and the heating schedule of ASHP, HN and HI. Finally, the proposed method was validated by calculation examples, and the results showed that the proposed method is beneficial for improving the energy economy and energy efficiency of building clusters.

Human-Infrastructure Interactions during the COVID-19 Pandemic: Understanding Water and Electricity Demand Profiles at the Building Level

When engineers design and manage a building's water and electricity utilities, they must make assumptions about resource use. These assumptions are often challenged when unexpected changes in demand occur, such as the spatial and temporal changes observed during the coronavirus (COVID-19) pandemic. Social distancing policies (SDPs) enacted led many universities to close their campuses and implement remote learning, impacting utility consumption patterns. Yet, little is known about how consumption changed at the building level. Here, we aim to understand how water and electricity consumption changed during the pandemic by identifying characteristic weekly demand profiles and understanding how these changes were related to regulatory and social systems. We performed k-means clustering on utility demand data measured before and as the pandemic evolved from five buildings of different types at the University of Texas at Austin. As expected, after SDPs were enacted both water and electricity use shifted, with most buildings seeing a sharp initial decline that remained low until the university partially reopened. In contrast to electricity use, we found that water use was tightly coupled with SDPs. Our study provides actionable information for managers to mitigate negative impacts (e.g., water stagnation) and capitalize on opportunities to minimize resource use.

Day Ahead Optimal Dispatch Schedule in a Smart Grid Containing Distributed Energy Resources and Electric Vehicles

This paper presents a day ahead optimal dispatch method for smart grids including two-axis tracking photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS) and electric vehicles (EV), which serve as additional storage systems in vehicle to grid (V2G) mode. The aim of the day ahead schedule is the minimization of fuel-based energy, imported from the main grid. The feasibility of the proposed method lies on the extensive communication network of the smart grids, including sensors and metering devices, that provide valuable information regarding the production of the distributed energy resources (DER), the energy consumption and the behavior of EV users. The day ahead optimal dispatch method is applied on a smart grid in order to showcase its effectiveness in terms of sustainability, full exploitation of DER production and ability of EVs to act as prosumers.

Optimal Scheduling of Campus Microgrid Considering the Electric Vehicle Integration in Smart Grid

High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by nearly 45%. The impact of EV as a storage tool is also observed. EVs acting as a source of energy reduced the energy cost by 45.58% and as a load by 19.33%. The impact on the cost for continuous power supply in case of a power outage is also analyzed.

Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System

This paper introduces an energy management strategy for an off-grid hybrid energy system. The hybrid system consists of a photovoltaic (PV) module, a LiFePO4 battery pack coupled with a Battery Management System (BMS), a hybrid solar inverter, and a load management control unit. A Long Short-Term Memory network (LSTM)-based forecasting strategy is implemented to predict the available PV and battery power. The learning data are extracted from an African country with a tropical climate, which is very suitable for PV power applications. Using LSTM as a prediction method significantly increases the efficiency of the forecasting. The main objective of the proposed strategy is to control the different loads according to the forecasted energy availability of the system and the forecasted battery state of charge (SOC). The proposed management algorithm and the system are tested using Matlab/Simulink software. A comparative study demonstrates that the reduction in the energy deficit of the system is approximately 53% compared to the system without load management. In addition to this, the reliability of the system is improved as the loss of power supply probability (LPSP) decreases from 5% to 3%.

Comprehensive early warning strategies based on consistency deviation of thermal-electrical characteristics for energy storage grid

Lithium iron phosphate (LiFePO₄) batteries have been dominant in energy storage systems. However, it is difficult to estimate the state of charge (SOC) and safety early warning of the batteries. To solve these problems, this paper developed a multiple timescale comprehensive early warning strategy based on the consistency deviation of the electrical and thermal characteristics of LiFePO₄ batteries. The unscented Kalman filter method was employed to estimate the battery SOC. The established comprehensive early warning strategy was verified through fault-triggered experiments at different time scales with different equivalent resistances. The results show that the comprehensive early warning strategy can realize early warning for different timescale failures of LiFePO₄ batteries under different energy storage conditions. For more dangerous severe failures that can break the safety valve, safety early warning can be realized 15 min in advance. This study provides a reference to ensure safe and reliable operations of energy storage systems.

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A comprehensive early warning strategy for multiple timescales was developed

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The battery electric-thermal characteristics of different time scales were obtained

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The proposed warning strategy expanded the application of early warning algorithms

The Mystery of Energy Compensation

AbstractThe received wisdom on how activity affects energy expenditure is that the more activity is undertaken, the more calories will have been burned by the end of the day. Yet traditional hunter-gatherers, who lead physically hard lives, burn no more calories each day than Western populations living in labor-saving environments. Indeed, there is now a wealth of data, both for humans and other animals, demonstrating that long-term lifestyle changes involving increases in exercise or other physical activities do not result in commensurate increases in daily energy expenditure (DEE). This is because humans and other animals exhibit a degree of energy compensation at the organismal level, ameliorating some of the increases in DEE that would occur from the increased activity by decreasing the energy expended on other biological processes. And energy compensation can be sizable, reaching many hundreds of calories in humans. But the processes that are downregulated in the long-term to achieve energy compensation are far from clear, particularly in humans-we do not know how energy compensation is achieved. My review here of the literature on relevant exercise intervention studies, for both humans and other species, indicates conflict regarding the role, if any, of basal metabolic rate (BMR) or low-level activity such as fidgeting play, particularly once changes in body composition are factored out. In situations where BMR and low-level activity are not major components of energy compensation, what then drives it? I discuss how changes in mitochondrial efficiency and changes in circadian fluctuations in BMR may contribute to our understanding of energy management. Currently unexplored, these mechanisms and others may provide important insights into the mystery of how energy compensation is achieved.

Training clinicians in a problem-solving fatigue programme for patients receiving maintenance haemodialysis

BACKGROUND

Personal Energy Planning is a problem-solving based programme that guides people receiving maintenance haemodialysis treatment to use energy management strategies to address life participation challenges. The feasibility of training dialysis clinicians to become Personal Energy Planning coaches (i.e., programme administrators) is currently unknown.

OBJECTIVES

To explore the feasibility of training dialysis clinicians to administer the Personal Energy Planning programme.

DESIGN

Feasibility study involving an adherence evaluation of two trained dialysis clinician coaches' problem-solving facilitation skills, and one qualitative interview with each coach.

PARTICIPANTS

Two Personal Energy Planning coaches with nursing backgrounds who administered the programme to 10 patients receiving maintenance haemodialysis treatment over a total of 34 sessions.

APPROACH

Audio recordings of one session per treatment recipient (n = 10) were evaluated using an established treatment adherence checklist. The proportion of treatment sessions where the item was observed by two adherence raters was calculated. In addition, coaches were interviewed about their experiences learning and administering the programme; interviews were analysed using inductive thematic analysis.

FINDINGS

Some core facilitation skills (e.g., patient-centred goal setting and analysis of performance breakdowns) were consistently used; however, other facilitation skills (e.g., guided discovery and global problem-solving strategy) were not regularly implemented. The coaches discussed challenges (e.g., supporting patient problem-solving and fluctuating patient health) with administering the intervention. Certain training resources (e.g., coaching handbook and expert consultation) were identified as valuable to their learning.

CONCLUSIONS

With modifications to training materials, it might be feasible to train dialysis clinicians to administer Personal Energy Planning with people receiving maintenance haemodialysis treatment.

A Bidirectional Versatile Buck-Boost Converter Driver for Electric Vehicle Applications

This work presents a novel dc-dc bidirectional buck-boost converter between a battery pack and the inverter to regulate the dc-bus in an electric vehicle (EV) powertrain. The converter is based on the versatile buck-boost converter, which has shown an excellent performance in different fuel cell systems operating in low-voltage and hard-switching applications. Therefore, extending this converter to higher voltage applications such as the EV is a challenging task reported in this work. A high-efficiency step-up/step-down versatile converter can improve the EV powertrain efficiency for an extended range of electric motor (EM) speeds, comprising urban and highway driving cycles while allowing the operation under motoring and regeneration (regenerative brake) conditions. DC-bus voltage regulation is implemented using a digital two-loop control strategy. The inner feedback loop is based on the discrete-time sliding-mode current control (DSMCC) strategy, and for the outer feedback loop, a proportional-integral (PI) control is employed. Both digital control loops and the necessary transition mode strategy are implemented using a digital signal controller TMS320F28377S. The theoretical analysis has been validated on a 400 V 1.6 kW prototype and tested through simulation and an EV powertrain system testing.

Coordination of Macro Base Stations for 5G Network with User Clustering

With the increasing amounts of terminal equipment with higher requirements of communication quality in the emerging fifth generation mobile communication network (5G), the energy consumption of 5G base stations (BSs) is increasing significantly, which not only raises the operating expenses of telecom operators but also imposes a burden on the environment. To solve this problem, a two-step energy management method that coordinates 5G macro BSs for 5G networks with user clustering is proposed. The coordination among the communication equipment and the standard equipment in 5G macro BSs is developed to reduce both the energy consumption and the electricity costs. A novel user clustering method is proposed together with Benders decomposition to accelerate the solving process. Simulation results show that the proposed method is computationally efficient and can ensure near-optimal performance, effectively reducing the energy consumption and electricity costs compared with the conventional dispatching scheme.

Energy Management Strategy of a Hybrid Power System Based on V2X Vehicle Speed Prediction

Energy consumption in vehicle driving is greatly influenced by traffic scenarios, and the intelligent traffic system (ITS) has a key role in solving the real-time optimal control of hybrid vehicles. To this end, a new energy management control strategy based on vehicle-to-everything (V2X) communication for vehicle speed prediction was proposed to dynamically adjust the engine and motor power output according to the traffic conditions. This study is based on intelligent network connectivity technology to obtain forward traffic state data and use a deep learning algorithm to model vehicle speed prediction using the traffic state data. The energy economy function was modeled using the MATLAB/Sinumlink platform and validated with a plug-in hybrid vehicle model simulation. The results indicate that the proposed strategy improves the vehicle energy economy by 13.02% and reduces CO₂ emissions by 16.04% under real vehicle driving conditions, compared with the conventional logic threshold-based control strategy.