564 research outputs found
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review
The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include “integrated energy system”, “multi-energy system”, or “smart energy system”. These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process
Decision support for participation in electricity markets considering the transaction of services and electricity at the local level
[EN] The growing concerns regarding the lack of fossil fuels, their costs, and their
impact on the environment have led governmental institutions to launch energy
policies that promote the increasing installation of technologies that use
renewable energy sources to generate energy. The increasing penetration of
renewable energy sources brings a great fluctuation on the generation side,
which strongly affects the power and energy system management. The control of
this system is moving from hierarchical and central to a smart and distributed
approach. The system operators are nowadays starting to consider the final end users (consumers and prosumers) as a part of the solution in power system
operation activities. In this sense, the end-users are changing their behavior from
passive to active players. The role of aggregators is essential in order to empower
the end-users, also contributing to those behavior changes. Although in several
countries aggregators are legally recognized as an entity of the power and energy
system, its role being mainly centered on representing end-users in wholesale
market participation.
This work contributes to the advancement of the state-of-the-art with
models that enable the active involvement of the end-users in electricity markets
in order to become key participants in the management of power and energy
systems. Aggregators are expected to play an essential role in these models,
making the connection between the residential end-users, electricity markets,
and network operators. Thus, this work focuses on providing solutions to a wide
variety of challenges faced by aggregators.
The main results of this work include the developed models to enable
consumers and prosumers participation in electricity markets and power and
energy systems management. The proposed decision support models consider
demand-side management applications, local electricity market models,
electricity portfolio management, and local ancillary services.
The proposed models are validated through case studies based on real data.
The used scenarios allow a comprehensive validation of the models from
different perspectives, namely end-users, aggregators, and network operators.
The considered case studies were carefully selected to demonstrate the characteristics of each model, and to demonstrate how each of them contributes
to answering the research questions defined to this work.[ES] La creciente preocupación por la escasez de combustibles fósiles, sus costos
y su impacto en el medio ambiente ha llevado a las instituciones
gubernamentales a lanzar políticas energéticas que promuevan la creciente
instalación de tecnologías que utilizan fuentes de energía renovables para
generar energía. La creciente penetración de las fuentes de energía renovable trae
consigo una gran fluctuación en el lado de la generación, lo que afecta
fuertemente la gestión del sistema de potencia y energía. El control de este
sistema está pasando de un enfoque jerárquico y central a un enfoque inteligente
y distribuido. Actualmente, los operadores del sistema están comenzando a
considerar a los usuarios finales (consumidores y prosumidores) como parte de
la solución en las actividades de operación del sistema eléctrico. En este sentido,
los usuarios finales están cambiando su comportamiento de jugadores pasivos a
jugadores activos. El papel de los agregadores es esencial para empoderar a los
usuarios finales, contribuyendo también a esos cambios de comportamiento.
Aunque en varios países los agregadores están legalmente reconocidos como una
entidad del sistema eléctrico y energético, su papel se centra principalmente en
representar a los usuarios finales en la participación del mercado mayorista.
Este trabajo contribuye al avance del estado del arte con modelos que
permiten la participación activa de los usuarios finales en los mercados eléctricos
para convertirse en participantes clave en la gestión de los sistemas de potencia
y energía. Se espera que los agregadores desempeñen un papel esencial en estos
modelos, haciendo la conexión entre los usuarios finales residenciales, los
mercados de electricidad y los operadores de red. Por lo tanto, este trabajo se
enfoca en brindar soluciones a una amplia variedad de desafíos que enfrentan los
agregadores.
Los principales resultados de este trabajo incluyen los modelos
desarrollados para permitir la participación de los consumidores y prosumidores
en los mercados eléctricos y la gestión de los sistemas de potencia y energía. Los
modelos de soporte de decisiones propuestos consideran aplicaciones de gestión
del lado de la demanda, modelos de mercado eléctrico local, gestión de cartera
de electricidad y servicios auxiliares locales.
Los modelos propuestos son validan mediante estudios de casos basados en
datos reales. Los escenarios utilizados permiten una validación integral de los
modelos desde diferentes perspectivas, a saber, usuarios finales, agregadores y
operadores de red. Los casos de estudio considerados fueron cuidadosamente
seleccionados para demostrar las características de cada modelo y demostrar
cómo cada uno de ellos contribuye a responder las preguntas de investigación
definidas para este trabajo
Smart Microgrids: Overview and Outlook
The idea of changing our energy system from a hierarchical design into a set
of nearly independent microgrids becomes feasible with the availability of
small renewable energy generators. The smart microgrid concept comes with
several challenges in research and engineering targeting load balancing,
pricing, consumer integration and home automation. In this paper we first
provide an overview on these challenges and present approaches that target the
problems identified. While there exist promising algorithms for the particular
field, we see a missing integration which specifically targets smart
microgrids. Therefore, we propose an architecture that integrates the presented
approaches and defines interfaces between the identified components such as
generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid
Worksho
A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings
Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio
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