157 research outputs found
Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions
Technology solutions must effectively balance economic growth, social equity,
and environmental integrity to achieve a sustainable society. Notably, although
the Internet of Things (IoT) paradigm constitutes a key sustainability enabler,
critical issues such as the increasing maintenance operations, energy
consumption, and manufacturing/disposal of IoT devices have long-term negative
economic, societal, and environmental impacts and must be efficiently
addressed. This calls for self-sustainable IoT ecosystems requiring minimal
external resources and intervention, effectively utilizing renewable energy
sources, and recycling materials whenever possible, thus encompassing energy
sustainability. In this work, we focus on energy-sustainable IoT during the
operation phase, although our discussions sometimes extend to other
sustainability aspects and IoT lifecycle phases. Specifically, we provide a
fresh look at energy-sustainable IoT and identify energy provision, transfer,
and energy efficiency as the three main energy-related processes whose
harmonious coexistence pushes toward realizing self-sustainable IoT systems.
Their main related technologies, recent advances, challenges, and research
directions are also discussed. Moreover, we overview relevant performance
metrics to assess the energy-sustainability potential of a certain technique,
technology, device, or network and list some target values for the next
generation of wireless systems. Overall, this paper offers insights that are
valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the
Communications Societ
The Influence of Electric Vehicle Availability on Vehicle-to-Grid Provision within a Microgrid.
By 2030, the number of electric vehicles (EVs) on the road is expected to increase to 11 million in the UK, meaning that there will be an increase in electricity demand. A potential solution to help manage this increase in demand is to use a technology called vehicle-to-grid (V2G) which is essentially a connection post that allows a bidirectional flow of energy, which means that EVs can charge and discharge when connected. Through this technology, the electrical grid can make use of the energy already stored in the battery of the EV.
This research aimed to understand the effects of EV availability on V2G technology within a microgrid and evaluated the feasibility of providing ancillary services. A predictive model, primarily trained on internal combustion engine vehicle (ICEV) trips, used the UK’s historical travel data to predict the location of EVs, achieving significant understanding of travel behaviour and EV availability. Split into two tasks—predicting start and end locations—this model utilised light gradient boosting machine (LightGBM) due to its superior performance. After fine-tuning, it yielded a weighted average F1 score of 0.900 and 0.902 for tasks 1 and 2, respectively. The model, when informed by new, real-world EV data, derived travel start and end locations, which was the fed into an optimisation model.
This optimisation model use a mixed integer linear programming (MILP) approach to schedule EV battery usage at the household level and study various case studies involving V2G technology. Simulations factored in different photovoltaic (PV) penetration rates, energy tariffs, and peer-to-peer (P2P) pricing mechanisms within a microgrid. First, the technical and economic benefits of home batteries, smart charging (V1G), and Vehicle-to-home (V2H) systems in EVs were evaluated, with an emphasis on performance and electricity bill reduction. The second case studied the potential of EVs to provide short term operation reserve (STOR) services. The third case explored a payment mechanism to optimise the state of charge (SOC) for EVs under V1G and V2H technologies for a week and estimate the energy available for restoration services.
The study reveals that both stationary home batteries and EVs, when integrated with solar power and dynamic tariffs, can effectively reduce electricity costs, despite the fluctuating availability of EVs. Notably, EVs, when combined with P2P energy sharing and V2H systems, offer comparable performance to stationary batteries, in addition to their transportation benefit. In terms of STOR provision, EVs meet the technical requirements, with their availability significantly influencing STOR provision. Factors like energy tariffs, solar power penetration rates, and P2P mechanisms have minimal effect on the STOR energy amount, but they do affect the overall microgrid performance. The study also highlights the need to maintain a 15% surplus of EVs within the microgrid for ensured resilience. Effective strategies to maintain a high SOC in EVs include higher payment rate systems, implementation of V1G and V2H strategies, and dynamic energy tariffs. The study, however, recommends limiting users to V1G to prioritise potential energy use for restoration services. Although EV availability affects the minimum SOC, it is not more significant than other factors such as EV penetration rates, energy tariffs, and P2P price mechanisms.
The findings imply that EV availability can reduce some of the benefits that stationary home battery have, such as surplus noon charging, while V2H might match home batteries in certain situations. EVs can offer STOR services as the fulfil most of the technical requirements, but the energy amount is dependant on available EVs during STOR events. EV availability had minimal effect on maintaining minimum SOC for a week that could potentially be used for restoration services, with energy tariffs and end-of-week incentives being more influential. Different PV penetration rates, energy tariffs, and P2P price mechanisms each have varied impacts on grid performance and V2G provision depending on the scenario
Peer-to-Peer Energy Trading in Smart Residential Environment with User Behavioral Modeling
Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.
Trading energy among users in a decentralized fashion has been referred to as Peer- to-Peer (P2P) Energy Trading, which has attracted significant attention from the research and industry communities in recent times. However, previous research has mostly focused on engineering aspects of P2P energy trading systems, often neglecting the central role of users in such systems. P2P trading mechanisms require active participation from users to decide factors such as selling prices, storing versus trading energy, and selection of energy sources among others. The complexity of these tasks, paired with the limited cognitive and time capabilities of human users, can result sub-optimal decisions or even abandonment of such systems if performance is not satisfactory. Therefore, it is of paramount importance for P2P energy trading systems to incorporate user behavioral modeling that captures users’ individual trading behaviors, preferences, and perceived utility in a realistic and accurate manner. Often, such user behavioral models are not known a priori in real-world settings, and therefore need to be learned online as the P2P system is operating.
In this thesis, we design novel algorithms for P2P energy trading. By exploiting a variety of statistical, algorithmic, machine learning, and behavioral economics tools, we propose solutions that are able to jointly optimize the system performance while taking into account and learning realistic model of user behavior. The results in this dissertation has been published in IEEE Transactions on Green Communications and Networking 2021, Proceedings of IEEE Global Communication Conference 2022, Proceedings of IEEE Conference on Pervasive Computing and Communications 2023 and ACM Transactions on Evolutionary Learning and Optimization 2023
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico
Conference proceedings info:
ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies
Raleigh, HI, United States, March 24-26, 2023
Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center
of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologÃas de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clÃnicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la SecretarÃa de Salud, el Centro de Comando, Comunicaciones y Control Informático.
de la SecretarÃa del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
PROCEEDINGS 5th PLATE Conference
The 5th international PLATE conference (Product Lifetimes and the Environment) addressed product lifetimes in the context of sustainability. The PLATE conference, which has been running since 2015, has successfully been able to establish a solid network of researchers around its core theme. The topic has come to the forefront of current (political, scientific & societal) debates due to its interconnectedness with a number of recent prominent movements, such as the circular economy, eco-design and collaborative consumption. For the 2023 edition of the conference, we encouraged researchers to propose how to extend, widen or critically re-construct thematic sessions for the PLATE conference, and the paper call was constructed based on these proposals. In this 5th PLATE conference, we had 171 paper presentations and 238 participants from 14 different countries. Beside of paper sessions we organized workshops and REPAIR exhibitions
Edge intelligence in smart grids : a survey on architectures, offloading models, cyber security measures, and challenges
The rapid development of new information and communication technologies (ICTs) and
the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work
has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge
computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article,
we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range
of angles, including architectures, computation offloading, and cybersecurity c oncerns. The basic
objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight
contemporary concepts closely related to edge computing, fundamental characteristics, and essential
enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided
in optimizing the performance of edge computing. We have emphasized the important enabling
technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both
general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic
questions about computation offloading are discussed: what is computation offloading and why do
we need it? Additionally, we divided the primary articles into two categories based on the number of
users included in the model, either a single user or a multiple user instance. Finally, we review the
cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore,
this survey comes to the conclusion that most of the viable architectures for EI in smart grids often
consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading
techniques must be framed as optimization problems and addressed effectively in order to increase
system performance. This article typically intends to serve as a primer for emerging and interested
scholars concerned with the study of EI in SGs.The Council for Scientific and Industrial Research (CSIR).https://www.mdpi.com/journal/jsanElectrical, Electronic and Computer Engineerin
Measuring knowledge sharing processes through social network analysis within construction organisations
The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable
to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes
Residential Demand Side Management model, optimization and future perspective: A review
The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints
Stochastic Model Predictive Control and Machine Learning for the Participation of Virtual Power Plants in Simultaneous Energy Markets
The emergence of distributed energy resources in the electricity system involves new scenarios in which domestic consumers (end-users) can be aggregated to participate in energy markets, acting as prosumers. Every prosumer is considered to work as an individual energy node, which has its own renewable generation source, its controllable and non-controllable energy loads, or even its own individual tariffs to trade. The nodes can build aggregations which are managed by a system operator.
The participation in energy markets is not trivial for individual prosumers due to different aspects such as the technical requirements which must be satisfied, or the need to trade with a minimum volume of energy. These requirements can be solved by the definition of aggregated participations.
In this context, the aggregators handle the difficult task of coordinating and stabilizing the prosumers' operations, not only at an individual level, but also at a system level, so that the set of energy nodes behaves as a single entity with respect to the market. The system operators can act as a trading-distributing company, or only as a trading one. For this reason, the optimization model must consider not only aggregated tariffs, but also individual tariffs to allow individual billing for each energy node. The energy node must have the required technical and legal competences, as well as the necessary equipment to manage their participation in energy markets or to delegate it to the system operator. This aggregation, according to business rules and not only to physical locations, is known as virtual power plant.
The optimization of the aggregated participation in the different energy markets requires the introduction of the concept of dynamic storage virtualization. Therefore, every energy node in the system under study will have a battery installed to store excess energy. This dynamic virtualization defines logical partitions in the storage system to allow its use for different purposes. As an example, two different partitions can be defined: one for the aggregated participation in the day-ahead market, and the other one for the demand-response program.
There are several criteria which must be considered when defining the participation strategy. A risky strategy will report more benefits in terms of trading; however, this strategy will also be more likely to get penalties for not meeting the contract due to uncertainties or operation errors. On the other hand, a conservative strategy would result worse economically in terms of trading, but it will reduce these potential penalties. The inclusion of dynamic intent profiles allows to set risky bids when there exist a potential low error of forecast in terms of generation, load or failures; and conservative bids otherwise.
The system operator is the agent who decides how much energy will be reserved to trade, how much to energy node self consumption, how much to demand-response program participation etc. The large number of variables and states makes this problem too complex to be solved by classical methods, especially considering the fact that slight differences in wrong decisions would imply important economic issues in the short term.
The concept of dynamic storage virtualization has been studied and implemented to allow the simultaneous participation in multiple energy markets. The simultaneous participations can be optimized considering the objective of potential profits, potential risks or even a combination of both considering more advanced criteria related to the system operator's know-how.
Day-ahead bidding algorithms, demand-response program participation optimization and a penalty-reduction operation control algorithm have been developed. A stochastic layer has been defined and implemented to improve the robustness inherent to forecast-dependent systems. This layer has been developed with chance-constraints, which includes the possibility of combining an intelligent agent based on a encoder-decoder arquitecture built with neural networks composed of gated recurrent units.
The formulation and the implementation allow a total decouplement among all the algorithms without any dependency among them. Nevertheless, they are completely engaged because the individual execution of each one considers both the current scenario and the selected strategy. This makes possible a wider and better context definition and a more real and accurate situation awareness.
In addition to the relevant simulation runs, the platform has also been tested on a real system composed of 40 energy nodes during one year in the German island of Borkum. This experience allowed the extraction of very satisfactory conclusions about the deployment of the platform in real environments.La irrupción de los sistemas de generación distribuidos en los sistemas eléctricos dan
lugar a nuevos escenarios donde los consumidores domésticos (usuarios finales)
pueden participar en los mercados de energÃa actuando como prosumidores. Cada prosumidor
es considerado como un nodo de energÃa con su propia fuente de generación de
energÃa renovable, sus cargas controlables y no controlables e incluso sus propias tarifas.
Los nodos pueden formar agregaciones que serán gestionadas por un agente denominado
operador del sistema.
La participación en los mercados energéticos no es trivial, bien sea por requerimientos
técnicos de instalación o debido a la necesidad de cubrir un volumen mÃnimo de energÃa por
transacción, que cada nodo debe cumplir individualmente. Estas limitaciones hacen casi
imposible la participación individual, pero pueden ser salvadas mediante participaciones
agregadas.
El agregador llevará a cabo la ardua tarea de coordinar y estabilizar las operaciones de los
nodos de energÃa, tanto individualmente como a nivel de sistema, para que todo el conjunto
se comporte como una unidad con respecto al mercado. Las entidades que gestionan
el sistema pueden ser meras comercializadoras, o distribuidoras y comercializadoras
simultáneamente. Por este motivo, el modelo de optimización sobre el que basarán sus
decisiones deberá considerar, además de las tarifas agregadas, otras individuales para
permitir facturaciones independientes. Los nodos deberán tener autonomÃa legal y técnica,
asà como el equipamiento necesario y suficiente para poder gestionar, o delegar en el
operador del sistema, su participación en los mercados de energÃa. Esta agregación
atendiendo a reglas de negocio y no solamente a restricciones de localización fÃsica es lo
que se conoce como Virtual Power Plant.
La optimización de la participación agregada en los mercados, desde el punto de
vista técnico y económico, requiere de la introducción del concepto de virtualización
dinámica del almacenamiento, para lo que será indispensable que los nodos pertenecientes
al sistema bajo estudio consten de una baterÃa para almacenar la energÃa sobrante. Esta
virtualización dinámica definirá particiones lógicas en el sistema de almacenamiento para
dedicar diferentes porcentajes de la energÃa almacenada para propósitos distintos. Como
ejemplo, se podrÃa hacer una virtualización en dos particiones lógicas diferentes: una de demand-response. AsÃ, el sistema podrÃa operar y satisfacer ambos mercados de
manera simultánea con el mismo grid y el mismo almacenamiento. El potencial de estas
particiones lógicas es que se pueden definir de manera dinámica, dependiendo del contexto
de ejecución y del estado, tanto de la red, como de cada uno de los nodos a nivel individual.
Para establecer una estrategia de participación se pueden considerar apuestas arriesgadas
que reportarán más beneficios en términos de compra-venta, pero también posibles
penalizaciones por no poder cumplir con el contrato. Por el contrario, una estrategia
conservadora podrÃa resultar menos beneficiosa económicamente en dichos términos de
compra-venta, pero reducirá las penalizaciones. La inclusión del concepto de perfiles de
intención dinámicos permitirá hacer pujas que sean arriesgadas, cuando existan errores de
predicción potencialmente pequeños en términos de generación, consumo o fallos; y pujas
más conservadoras en caso contrario.
El operador del sistema es el agente que definirá cuánta energÃa utiliza para comercializar,
cuánta para asegurar autoconsumo, cuánta desea tener disponible para participar en el
programa de demand-response etc. El gran número de variables y de situaciones posibles
hacen que este problema sea muy costoso y complejo de resolver mediante métodos
clásicos, sobre todo teniendo en cuenta que pequeñas variaciones en la toma de decisiones
pueden tener grandes implicaciones económicas incluso a corto plazo.
En esta tesis se ha investigado en el concepto de virtualización dinámica del almacenamiento
para permitir una participación simultánea en múltiples mercados. La estrategia
de optimización definida permite participaciones simultáneas en diferentes mercados que
pueden ser controladas con el objetivo de optimizar el beneficio potencial, el riesgo potencial,
o incluso una combinación mixta de ambas en base a otros criterios más avanzados
marcados por el know-how del operador del sistema.
Se han desarrollado algoritmos de optimización para el mercado del day-ahead, para la
participación en el programa de demand-response y un algoritmo de control para reducir
las penalizaciones durante la operación mediante modelos de control predictivo. Se ha
realizado la definición e implementación de un componente estocástico para hacer el
sistema más robusto frente a la incertidumbre inherente a estos sistemas en los que hay
tanto peso de una componente de tipo forecasing. La formulación de esta capa se ha
realizado mediante chance-constraints, que incluye la posibilidad de combinar diferentes
componentes para mejorar la precisión de la optimización. Para el caso de uso presentado
se ha elegido la combinación de métodos estadÃsticos por probabilidad junto a un agente
inteligente basado en una arquitectura de codificador-decodificador construida con redes
neuronales compuestas de Gated Recurrent Units.
La formulación y la implementación utilizada permiten que, aunque todos los algoritmos
estén completamente desacoplados y no presenten dependencias entre ellos, todos se actual como la estrategia seleccionada. Esto permite la definición de un contexto mucho
más amplio en la ejecución de las optimizaciones y una toma de decisiones más consciente,
real y ajustada a la situación que condiciona al proceso.
Además de las pertinentes pruebas de simulación, parte de la herramienta ha sido
probada en un sistema real compuesto por 40 nodos domésticos, convenientemente equipados,
durante un año en una infraestructura implantada en la isla alemana de Borkum. Esta
experiencia ha permitido extraer conclusiones muy interesantes sobre la implantación de
la plataforma en entornos reales
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