210 research outputs found
Multiple-Periods Locally-Facet-Based MIP Formulations for the Unit Commitment Problem
The thermal unit commitment (UC) problem has historically been formulated as
a mixed integer quadratic programming (MIQP), which is difficult to solve
efficiently, especially for large-scale systems. The tighter characteristic
reduces the search space, therefore, as a natural consequence, significantly
reduces the computational burden. In literatures, many tightened formulations
for a single unit with parts of constraints were reported without presenting
explicitly how they were derived. In this paper, a systematic approach is
developed to formulate tight formulations. The idea is to use more binary
variables to represent the state of the unit so as to obtain the tightest upper
bound of power generation limits and ramping constraints for a single unit. In
this way, we propose a multi-period formulation based on sliding windows which
may have different sizes for each unit in the system. Furthermore, a
multi-period model taking historical status into consideration is obtained.
Besides, sufficient and necessary conditions for the facets of single-unit
constraints polytope are provided and redundant inequalities are eliminated.
The proposed models and three other state-of-the-art models are tested on 73
instances with a scheduling time of 24 hours. The number of generators in the
test systems ranges from 10 to 1080. The simulation results show that our
proposed multi-period formulations are tighter than the other three
state-of-the-art models when the window size of the multi-period formulation is
greater than 2.Comment: 76 pages, 18 figures, 10 tables. This work has been published in IEEE
Transactions on Power System
Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques
Energy forecasting has a vital role to play in smart grid (SG) systems
involving various applications such as demand-side management, load shedding,
and optimum dispatch. Managing efficient forecasting while ensuring the least
possible prediction error is one of the main challenges posed in the grid
today, considering the uncertainty and granularity in SG data. This paper
presents a comprehensive and application-oriented review of state-of-the-art
forecasting methods for SG systems along with recent developments in
probabilistic deep learning (PDL) considering different models and
architectures. Traditional point forecasting methods including statistical,
machine learning (ML), and deep learning (DL) are extensively investigated in
terms of their applicability to energy forecasting. In addition, the
significance of hybrid and data pre-processing techniques to support
forecasting performance is also studied. A comparative case study using the
Victorian electricity consumption and American electric power (AEP) datasets is
conducted to analyze the performance of point and probabilistic forecasting
methods. The analysis demonstrates higher accuracy of the long-short term
memory (LSTM) models with appropriate hyper-parameter tuning among point
forecasting methods especially when sample sizes are larger and involve
nonlinear patterns with long sequences. Furthermore, Bayesian bidirectional
LSTM (BLSTM) as a probabilistic method exhibit the highest accuracy in terms of
least pinball score and root mean square error (RMSE)
Context-TAP: Tracking Any Point Demands Spatial Context Features
We tackle the problem of Tracking Any Point (TAP) in videos, which
specifically aims at estimating persistent long-term trajectories of query
points in videos. Previous methods attempted to estimate these trajectories
independently to incorporate longer image sequences, therefore, ignoring the
potential benefits of incorporating spatial context features. We argue that
independent video point tracking also demands spatial context features. To this
end, we propose a novel framework Context-TAP, which effectively improves point
trajectory accuracy by aggregating spatial context features in videos.
Context-TAP contains two main modules: 1) a SOurse Feature Enhancement (SOFE)
module, and 2) a TArget Feature Aggregation (TAFA) module. Context-TAP
significantly improves PIPs all-sided, reducing 11.4% Average Trajectory Error
of Occluded Points (ATE-Occ) on CroHD and increasing 11.8% Average Percentage
of Correct Keypoint (A-PCK) on TAP-Vid-Kinectics. Demos are available at this
.Comment: Project Page: this
$\href{https://wkbian.github.io/Projects/Context-TAP/}{webpage}
A hybrid Planning Method for Transmission Network in a Deregulated Enviroment
The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system and relevant analyses and discussions have been presented
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