18,525 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
A Self-adaptive Agent-based System for Cloud Platforms
Cloud computing is a model for enabling on-demand network access to a shared
pool of computing resources, that can be dynamically allocated and released
with minimal effort. However, this task can be complex in highly dynamic
environments with various resources to allocate for an increasing number of
different users requirements. In this work, we propose a Cloud architecture
based on a multi-agent system exhibiting a self-adaptive behavior to address
the dynamic resource allocation. This self-adaptive system follows a MAPE-K
approach to reason and act, according to QoS, Cloud service information, and
propagated run-time information, to detect QoS degradation and make better
resource allocation decisions. We validate our proposed Cloud architecture by
simulation. Results show that it can properly allocate resources to reduce
energy consumption, while satisfying the users demanded QoS
Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems
Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions
Perspectives of Integrated “Next Industrial Revolution” Clusters in Poland and Siberia
Rozdział z: Functioning of the Local Production Systems in Central and Eastern European Countries and Siberia. Case Studies and Comparative Studies, ed. Mariusz E. Sokołowicz.The paper presents the mapping of potential next industrial revolution clusters in Poland and Siberia. Deindustrialization of the cities and struggles with its consequences are one of the fundamental economic problems in current global economy. Some hope to find an answer to that problem is associated with the idea of next industrial revolution and reindustrialization initiatives. In the paper, projects aimed at developing next industrial revolution clusters are analyzed. The objective of the research was to examine new industrial revolution paradigm as a platform for establishing university-based trans-border industry clusters in Poland and Siberia47 and to raise awareness of next industry revolution initiatives.Monograph financed under a contract of execution of the international scientific project within 7th Framework Programme of the European Union, co-financed by Polish Ministry of Science and Higher Education (title: “Functioning of the Local Production Systems in the Conditions of Economic Crisis (Comparative Analysis and Benchmarking for the EU and Beyond”)). Monografia sfinansowana w oparciu o umowę o wykonanie projektu między narodowego w ramach 7. Programu Ramowego UE, współfinansowanego ze środków Ministerstwa Nauki i Szkolnictwa Wyższego (tytuł projektu: „Funkcjonowanie lokalnych systemów produkcyjnych w warunkach kryzysu gospodarczego (analiza porównawcza i benchmarking w wybranych krajach UE oraz krajach trzecich”))
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