46,050 research outputs found
Stochastic optimisation-based valuation of smart grid options under firm DG contracts
Under the current EU legislation, Distribution Network Operators (DNOs) are expected to provide firm connections to new DG, whose penetration is set to increase worldwide creating the need for significant investments to enhance network capacity. However, the uncertainty around the magnitude, location and timing of future DG capacity renders planners unable to accurately determine in advance where network violations may occur. Hence, conventional network reinforcements run the risk of asset stranding, leading to increased integration costs. A novel stochastic planning model is proposed that includes generalized formulations for investment in conventional and smart grid assets such as Demand-Side Response (DSR), Coordinated Voltage Control (CVC) and Soft Open Point (SOP) allowing the quantification of their option value. We also show that deterministic planning approaches may underestimate or completely ignore smart technologies
Practical issues for the implementation of survivability and recovery techniques in optical networks
Trajectory-Based Off-Policy Deep Reinforcement Learning
Policy gradient methods are powerful reinforcement learning algorithms and
have been demonstrated to solve many complex tasks. However, these methods are
also data-inefficient, afflicted with high variance gradient estimates, and
frequently get stuck in local optima. This work addresses these weaknesses by
combining recent improvements in the reuse of off-policy data and exploration
in parameter space with deterministic behavioral policies. The resulting
objective is amenable to standard neural network optimization strategies like
stochastic gradient descent or stochastic gradient Hamiltonian Monte Carlo.
Incorporation of previous rollouts via importance sampling greatly improves
data-efficiency, whilst stochastic optimization schemes facilitate the escape
from local optima. We evaluate the proposed approach on a series of continuous
control benchmark tasks. The results show that the proposed algorithm is able
to successfully and reliably learn solutions using fewer system interactions
than standard policy gradient methods.Comment: Includes appendix. Accepted for ICML 201
Dovetail: Stronger Anonymity in Next-Generation Internet Routing
Current low-latency anonymity systems use complex overlay networks to conceal
a user's IP address, introducing significant latency and network efficiency
penalties compared to normal Internet usage. Rather than obfuscating network
identity through higher level protocols, we propose a more direct solution: a
routing protocol that allows communication without exposing network identity,
providing a strong foundation for Internet privacy, while allowing identity to
be defined in those higher level protocols where it adds value.
Given current research initiatives advocating "clean slate" Internet designs,
an opportunity exists to design an internetwork layer routing protocol that
decouples identity from network location and thereby simplifies the anonymity
problem. Recently, Hsiao et al. proposed such a protocol (LAP), but it does not
protect the user against a local eavesdropper or an untrusted ISP, which will
not be acceptable for many users. Thus, we propose Dovetail, a next-generation
Internet routing protocol that provides anonymity against an active attacker
located at any single point within the network, including the user's ISP. A
major design challenge is to provide this protection without including an
application-layer proxy in data transmission. We address this challenge in path
construction by using a matchmaker node (an end host) to overlap two path
segments at a dovetail node (a router). The dovetail then trims away part of
the path so that data transmission bypasses the matchmaker. Additional design
features include the choice of many different paths through the network and the
joining of path segments without requiring a trusted third party. We develop a
systematic mechanism to measure the topological anonymity of our designs, and
we demonstrate the privacy and efficiency of our proposal by simulation, using
a model of the complete Internet at the AS-level
Complex network analysis and nonlinear dynamics
This chapter aims at reviewing complex network and nonlinear dynamical
models and methods that were either developed for or applied to socioeconomic
issues, and pertinent to the theme of New Economic Geography. After an introduction
to the foundations of the field of complex networks, the present summary
introduces some applications of complex networks to economics, finance, epidemic
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issue
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