9,098 research outputs found
Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches
Peer-to-peer (P2P) energy trading has emerged as a next-generation energy
management mechanism for the smart grid that enables each prosumer of the
network to participate in energy trading with one another and the grid. This
poses a significant challenge in terms of modeling the decision-making process
of each participant with conflicting interest and motivating prosumers to
participate in energy trading and to cooperate, if necessary, for achieving
different energy management goals. Therefore, such decision-making process
needs to be built on solid mathematical and signal processing tools that can
ensure an efficient operation of the smart grid. This paper provides an
overview of the use of game theoretic approaches for P2P energy trading as a
feasible and effective means of energy management. As such, we discuss various
games and auction theoretic approaches by following a systematic classification
to provide information on the importance of game theory for smart energy
research. Then, the paper focuses on the P2P energy trading describing its key
features and giving an introduction to an existing P2P testbed. Further, the
paper zooms into the detail of some specific game and auction theoretic models
that have recently been used in P2P energy trading and discusses some important
finding of these schemes.Comment: 38 pages, single column, double spac
Self-Evaluation Applied Mathematics 2003-2008 University of Twente
This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008
Maximizing Profit in Green Cellular Networks through Collaborative Games
In this paper, we deal with the problem of maximizing the profit of Network
Operators (NOs) of green cellular networks in situations where
Quality-of-Service (QoS) guarantees must be ensured to users, and Base Stations
(BSs) can be shared among different operators. We show that if NOs cooperate
among them, by mutually sharing their users and BSs, then each one of them can
improve its net profit. By using a game-theoretic framework, we study the
problem of forming stable coalitions among NOs. Furthermore, we propose a
mathematical optimization model to allocate users to a set of BSs, in order to
reduce costs and, at the same time, to meet user QoS for NOs inside the same
coalition. Based on this, we propose an algorithm, based on cooperative game
theory, that enables each operator to decide with whom to cooperate in order to
maximize its profit. This algorithms adopts a distributed approach in which
each NO autonomously makes its own decisions, and where the best solution
arises without the need to synchronize them or to resort to a trusted third
party. The effectiveness of the proposed algorithm is demonstrated through a
thorough experimental evaluation considering real-world traffic traces, and a
set of realistic scenarios. The results we obtain indicate that our algorithm
allows a population of NOs to significantly improve their profits thanks to the
combination of energy reduction and satisfaction of QoS requirements.Comment: Added publisher info and citation notic
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
- …