506 research outputs found
Identifiability and Consistent Estimation for Gaussian Chain Graph Models
The chain graph model admits both undirected and directed edges in one graph,
where symmetric conditional dependencies are encoded via undirected edges and
asymmetric causal relations are encoded via directed edges. Though frequently
encountered in practice, the chain graph model has been largely under
investigated in literature, possibly due to the lack of identifiability
conditions between undirected and directed edges. In this paper, we first
establish a set of novel identifiability conditions for the Gaussian chain
graph model, exploiting a low rank plus sparse decomposition of the precision
matrix. Further, an efficient learning algorithm is built upon the
identifiability conditions to fully recover the chain graph structure.
Theoretical analysis on the proposed method is conducted, assuring its
asymptotic consistency in recovering the exact chain graph structure. The
advantage of the proposed method is also supported by numerical experiments on
both simulated examples and a real application on the Standard & Poor 500 index
data
Management Of Plug-In Electric Vehicles And Renewable Energy Sources In Active Distribution Networks
Near 160 million customers in the U.S.A. are served via distribution networks (DNs). The increasing penetration level of renewable energy sources (RES) and plug-in electric vehicles (PEVs), the implementation of smart distribution technologies such as advanced metering/monitoring infrastructure, and the adoption of smart appliances, have changed distribution networks from passive to active. The next-generation of DNs should be efficient and optimized system-wide, highly reliable and robust, and capable of effectively managing highly-penetrated PEVs, RES and other controllable loads. To meet new challenges, the next-generation DNs need active distribution management (ADM).
In this thesis, we study the management of PEVs and RES in active DNs. First, we propose a novel discrete-event modeling method to model PEVs and other loads in distribution networks. In addition, a new optimization algorithm to integrate as many PEVs as possible in DNs without causing voltage issues, including the violation of voltage security ranges and voltage stability, is studied. To further explore the active management of PEVs in the DNs, we develop a universal demonstration platform, consisting of software packages and hardware remote terminal units. The demonstration platform is designed with the capabilities of measurement, monitoring, control, automation, and communications.
Furthermore, we have studied the reactive power management in microgrids, a special platform to integrate distributed generations and energy storage in DNs. To solve possible voltage security issues in a microgrid with high penetration of single-phase induction machines under the condition of fault-induced islanding, a voltage-sensitivity-based reactive power management algorithm is proposed
An MAS Based Energy Management System for a Stand-Alone Microgrid at High Altitude
A multi-agent system based energy management system (EMS) is proposed in this paper for implementing a PV-small hydro hybrid microgrid (MG) at high altitude. Based on local information, the distributed generation (DG) sources in the MG are controlled via the EMS to achieve efficient and stable system operation. Virtual bidding is used to quickly establish the scheduling of system operation and capacity reserve. In addition, real-time power dispatches are carried out through model predictive control to balance load demand and power generation in the MG. The dynamic model and the energy management strategy of the MG have been simulated on a RTDS–PXI joint real-time simulation platform. The simulation results show that the proposed energy management and control strategy can optimally dispatch the DG sources in the MG to achieve economic and secure operations of the whole system
Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection
This paper introduces HPC-Net, a high-precision and rapidly convergent object
detection network.Comment: 10 pages,5 figures, conferenc
A Review of Active Management for Distribution Networks: Current Status and Future Development Trends
Driven by smart distribution technologies, by the widespread use of distributed generation sources, and by the injection of new loads, such as electric vehicles, distribution networks are evolving from passive to active. The integration of distributed generation, including renewable distributed generation changes the power flow of a distribution network from unidirectional to bi-directional. The adoption of electric vehicles makes the management of distribution networks even more challenging. As such, an active network management has to be fulfilled by taking advantage of the emerging techniques of control, monitoring, protection, and communication to assist distribution network operators in an optimal manner. This article presents a short review of recent advancements and identifies emerging technologies and future development trends to support active management of distribution networks
Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing
Key challenges in running a retail business include how to select products to
present to consumers (the assortment problem), and how to price products (the
pricing problem) to maximize revenue or profit. Instead of considering these
problems in isolation, we propose a joint approach to assortment-pricing based
on contextual bandits. Our model is doubly high-dimensional, in that both
context vectors and actions are allowed to take values in high-dimensional
spaces. In order to circumvent the curse of dimensionality, we propose a simple
yet flexible model that captures the interactions between covariates and
actions via a (near) low-rank representation matrix. The resulting class of
models is reasonably expressive while remaining interpretable through latent
factors, and includes various structured linear bandit and pricing models as
particular cases. We propose a computationally tractable procedure that
combines an exploration/exploitation protocol with an efficient low-rank matrix
estimator, and we prove bounds on its regret. Simulation results show that this
method has lower regret than state-of-the-art methods applied to various
standard bandit and pricing models. Real-world case studies on the
assortment-pricing problem, from an industry-leading instant noodles company to
an emerging beauty start-up, underscore the gains achievable using our method.
In each case, we show at least three-fold gains in revenue or profit by our
bandit method, as well as the interpretability of the latent factor models that
are learned
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