290,053 research outputs found
Robust Optimal Power Flow with Wind Integration Using Conditional Value-at-Risk
Integrating renewable energy into the power grid requires intelligent
risk-aware dispatch accounting for the stochastic availability of renewables.
Toward achieving this goal, a robust DC optimal flow problem is developed in
the present paper for power systems with a high penetration of wind energy. The
optimal dispatch is obtained as the solution to a convex program with a
suitable regularizer, which is able to mitigate the potentially high risk of
inadequate wind power. The regularizer is constructed based on the energy
transaction cost using conditional value-at-risk (CVaR). Bypassing the
prohibitive high-dimensional integral, the distribution-free sample average
approximation method is efficiently utilized for solving the resulting
optimization problem. Case studies are reported to corroborate the efficacy of
the novel model and approach tested on the IEEE 30-bus benchmark system with
real operation data from seven wind farms.Comment: To Appear in Proc. of the 4th Intl. Conf. on Smart Grid
Communication
Prompt Application-Transparent Transaction Revalidation in Software Transactional Memory
Software Transactional Memory (STM) allows encapsulating shared-data accesses within transactions, executed with atomicity and isolation guarantees. The assessment of the consistency of a running transaction is performed by the STM layer at specific points of its execution, such as when a read or write access to a shared object occurs, or upon a commit attempt. However, performance and energy efficiency issues may arise when no shared-data read/write operation occurs for a while along a thread running a transaction. In this scenario, the STM layer may not regain control for a considerable amount of time, thus not being able to early detect if such transaction has become inconsistent in the meantime. To tackle this problem we present an STM architecture that, thanks to a lightweight operating system support, is able to perform a fine-grain periodic (hence prompt) revalidation of running transactions. Our proposal targets Linux and x86 systems and has been integrated with the open source TinySTM package. Experimental results with a port of the TPC-C benchmark to STM environments show the effectiveness of our solution
A Bayesian Approach to Identify Bitcoin Users
Bitcoin is a digital currency and electronic payment system operating over a
peer-to-peer network on the Internet. One of its most important properties is
the high level of anonymity it provides for its users. The users are identified
by their Bitcoin addresses, which are random strings in the public records of
transactions, the blockchain. When a user initiates a Bitcoin-transaction, his
Bitcoin client program relays messages to other clients through the Bitcoin
network. Monitoring the propagation of these messages and analyzing them
carefully reveal hidden relations. In this paper, we develop a mathematical
model using a probabilistic approach to link Bitcoin addresses and transactions
to the originator IP address. To utilize our model, we carried out experiments
by installing more than a hundred modified Bitcoin clients distributed in the
network to observe as many messages as possible. During a two month observation
period we were able to identify several thousand Bitcoin clients and bind their
transactions to geographical locations
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