15 research outputs found
東北大学電気通信研究所研究活動報告 第29号(2022年度)
紀要類(bulletin)departmental bulletin pape
Business models in the Smart Grid: challenges, opportunities and proposals for prosumer profitability.
Considering that non-renewable energy resources are dwindling, the smart grid turns out to be one of the most promising and compelling systems for the future of energy. Not only does it combine efficient energy consumption with avant-garde technologies related to renewable energies, but it is also capable of providing several beneficial utilities, such as power monitoring and data provision. When smart grid end users turn into prosumers, they become arguably the most important value creators within the smart grid and a decisive agent of change in terms of electricity usage. There is a plethora of research and development areas related to the smart grid that can be exploited for new business opportunities, thus spawning another branch of the so-called ?green economy? focused on turning smart energy usage into a profitable business. This paper deals with emerging business models for smart grid prosumers, their strengths and weaknesses and puts forward new prosumer-oriented business models, along with their value propositions
Optimal design and control of stationary electrochemical double-layer capacitors for light railways
The optimisation algorithm has been further investigated to understand the influence of the weight coefficients that affect the solution of all the optimisation problems and it is very often overlooked in the traditional approach. In fact, the choice of weight coefficients leading to the optimum among different optimal solutions also presents a challenge and this specific problem does not give any a priori indications. This challenge has been tackled using both genetic algorithms and particle swarm optimisations, which are the best methods when there are multiple local optima and the number of parameters is large. The results show that, when the optimal set of coefficients are used and the optimal positions and capacitances of EDLCs are selected, the energy savings can be up to 42%.
The second problem of the control of the storage has been tackled with a linear state of charge control based on a piece-wise linear characteristic between the current and the voltage deviation from the nominal voltage of the supply at the point of connection of the storage. The simulations show that, regardless of the initial state of charge, the control maintain the state of charge of EDLCs within the prescribed range with no need of using the on-board braking resistor and, hence, dissipating braking
energy. The robustness of the control algorithm has been verified by changing the characteristics of the train loading and friction force, with an energy saving between 26 - 27%
Autonomous Drone Landings on an Unmanned Marine Vehicle using Deep Reinforcement Learning
This thesis describes with the integration of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV, also commonly known as drone) in a single Multi-Agent System (MAS). In marine robotics, the advantage offered by a MAS consists of exploiting the key features of a single robot to compensate for the shortcomings in the other. In this way, a USV can serve as the landing platform to alleviate the need for a UAV to be airborne for long periods time, whilst the latter can increase the overall environmental awareness thanks to the possibility to cover large portions of the prevailing environment with a camera (or more than one) mounted on it. There are numerous potential applications in which this system can be used, such as deployment in search and rescue missions, water and coastal monitoring, and reconnaissance and force protection, to name but a few.
The theory developed is of a general nature. The landing manoeuvre has been accomplished mainly identifying, through artificial vision techniques, a fiducial marker placed on a flat surface serving as a landing platform. The raison d'etre for the thesis was to propose a new solution for autonomous landing that relies solely on onboard sensors and with minimum or no communications between the vehicles. To this end, initial work solved the problem while using only data from the cameras mounted on the in-flight drone. In the situation in which the tracking of the marker is interrupted, the current position of the USV is estimated and integrated into the control commands. The limitations of classic control theory used in this approached suggested the need for a new solution that empowered the flexibility of intelligent methods, such as fuzzy logic or artificial neural networks. The recent achievements obtained by deep reinforcement learning (DRL) techniques in end-to-end control in playing the Atari video-games suite represented a fascinating while challenging new way to see and address the landing problem. Therefore, novel architectures were designed for approximating the action-value function of a Q-learning algorithm and used to map raw input observation to high-level navigation actions. In this way, the UAV learnt how to land from high latitude without any human supervision, using only low-resolution grey-scale images and with a level of accuracy and robustness. Both the approaches have been implemented on a simulated test-bed based on Gazebo simulator and the model of the Parrot AR-Drone. The solution based on DRL was further verified experimentally using the Parrot Bebop 2 in a series of trials. The outcomes demonstrate that both these innovative methods are both feasible and practicable, not only in an outdoor marine scenario but also in indoor ones as well
Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids
This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
Investigación sobre variables predictivas del mantenimiento de parques eólicos
Programa Oficial de Doutoramento en Enerxía e Propulsión Mariña. 5014P01[Resumo]
A presente tese doctoral presenta un novo procedemento para deseñar os plans de
mantemento, así como unha ferramenta de axuda para a toma de decisións operativas e
de mantemento nos parques eólicos. Dito procedemento baséase na búsqueda da
combinación de valores das variables climáticas que representan estados de alta
probabilidade de falla a través do uso de superficies de resposta. Estes estados de alta
probabilidade de falla representan un novo concepto, chamado como Indisponibilidade
Técnica Climática, que será definido e trataránse as posibles aplicacións do mesmo que
van desde a fase de deseño ata a fase de explotación dos parques eólicos.[Resumen]
La presente tesis doctoral presenta un nuevo procedimiento para diseñar planes de
mantenimiento, así como una herramienta de ayuda para la toma de decisiones
operativas y de mantenimiento en los parques eólicos. Dicho procedimiento está basado
en la búsqueda de la combinación de valores de las variables climáticas que representan
estados de alta probabilidad de fallo mediante el uso de superficies de respuesta. Estos
estados de alta probabilidad de fallo representan un nuevo concepto, denominado como
Indisponibilidad Técnica Climática, que será definido y se tratarán las posibles
aplicaciones del mismo que abarcan desde la fase de diseño hasta la fase de explotación de los parques eólicos.[Abstract]
The present doctoral thesis introduces a new procedure to design maintenance plans, as
well as a help tool for making operational and maintenance decisions in wind farms.
This procedure is based on the search of the combination of values of climatic variables
that represent states of high probability of failure through the use of response surfaces.
These states high probability of failure represent a new concept called as Unavailability
Climate Technical, which will be defined and possible applications thereof ranging
from the design phase to the operating phase of the wind farm will be discussed