11,270 research outputs found
Unsplittable Load Balancing in a Network of Charging Stations Under QoS Guarantees
The operation of the power grid is becoming more stressed, due to the
addition of new large loads represented by Electric Vehicles (EVs) and a more
intermittent supply due to the incorporation of renewable sources. As a
consequence, the coordination and control of projected EV demand in a network
of fast charging stations becomes a critical and challenging problem.
In this paper, we introduce a game theoretic based decentralized control
mechanism to alleviate negative impacts from the EV demand. The proposed
mechanism takes into consideration the non-uniform spatial distribution of EVs
that induces uneven power demand at each charging facility, and aims to: (i)
avoid straining grid resources by offering price incentives so that customers
accept being routed to less busy stations, (ii) maximize total revenue by
serving more customers with the same amount of grid resources, and (iii)
provide charging service to customers with a certain level of
Quality-of-Service (QoS), the latter defined as the long term customer blocking
probability. We examine three scenarios of increased complexity that gradually
approximate real world settings. The obtained results show that the proposed
framework leads to substantial performance improvements in terms of the
aforementioned goals, when compared to current state of affairs.Comment: Accepted for Publication in IEEE Transactions on Smart Gri
An Architecture for Distributed Energies Trading in Byzantine-Based Blockchain
With the development of smart cities, not only are all corners of the city
connected to each other, but also connected from city to city. They form a
large distributed network together, which can facilitate the integration of
distributed energy station (DES) and corresponding smart aggregators.
Nevertheless, because of potential security and privacy protection arisen from
trustless energies trading, how to make such energies trading goes smoothly is
a tricky challenge. In this paper, we propose a blockchain-based multiple
energies trading (B-MET) system for secure and efficient energies trading by
executing a smart contract we design. Because energies trading requires the
blockchain in B-MET system to have high throughput and low latency, we design a
new byzantine-based consensus mechanism (BCM) based on node's credit to improve
efficiency for the consortium blockchain under the B-MET system. Then, we take
combined heat and power (CHP) system as a typical example that provides
distributed energies. We quantify their utilities, and model the interactions
between aggregators and DESs in a smart city by a novel multi-leader
multi-follower Stackelberg game. It is analyzed and solved by reaching Nash
equilibrium between aggregators, which reflects the competition between
aggregators to purchase energies from DESs. In the end, we conduct plenty of
numerical simulations to evaluate and verify our proposed model and algorithms,
which demonstrate their correctness and efficiency completely
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Two Auction-Based Resource Allocation Environments: Design and Experience
Many computer systems have reached the point where the goal of resource
allocation is no longer to maximize utilization; instead, when demand
exceeds supply and not all needs can be met, one needs a policy to guide
resource allocation decisions. One natural policy is to seek efficient usage,
which allocates resources to the set of users who have the highest utility for
the use of the resources. Researchers have frequently proposed market-based
mechanisms to provide such a goal-oriented way to allocate resources
among competing interests while maximizing overall utility of the users.Engineering and Applied Science
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
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