7,735 research outputs found
Optimal Rotational Load Shedding via Bilinear Integer Programming
This paper addresses the problem of managing rotational load shedding
schedules for a power distribution network with multiple load zones. An integer
optimization problem is formulated to find the optimal number and duration of
planned power outages. Various types of damage costs are proposed to capture
the heterogeneous load shedding preferences of different zones. The McCormick
relaxation along with an effective procedure feasibility recovery is developed
to solve the resulting bilinear integer program, which yields a high-quality
suboptimal solution. Extensive simulation results corroborate the merit of the
proposed approach, which has a substantial edge over existing load shedding
schemes.Comment: 6 pages, 11 figures. To appear at the conference of APSIPA ASC 201
Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Outage scheduling aims at defining, over a horizon of several months to
years, when different components needing maintenance should be taken out of
operation. Its objective is to minimize operation-cost expectation while
satisfying reliability-related constraints. We propose a distributed
scenario-based chance-constrained optimization formulation for this problem. To
tackle tractability issues arising in large networks, we use machine learning
to build a proxy for predicting outcomes of power system operation processes in
this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains
cheaper and more reliable plans than other candidates
Component Outage Estimation based on Support Vector Machine
Predicting power system component outages in response to an imminent
hurricane plays a major role in preevent planning and post-event recovery of
the power system. An exact prediction of components states, however, is a
challenging task and cannot be easily performed. In this paper, a Support
Vector Machine (SVM) based method is proposed to help estimate the components
states in response to anticipated path and intensity of an imminent hurricane.
Components states are categorized into three classes of damaged, operational,
and uncertain. The damaged components along with the components in uncertain
class are then considered in multiple contingency scenarios of a proposed
Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the
simultaneous outage of multiple components under an N-m-u reliability
criterion. Experimental results on the IEEE 118-bus test system show the merits
and the effectiveness of the proposed SVM classifier and the E-SCUC model in
improving power system resilience in response to extreme events
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An Assessment of PIER Electric Grid Research 2003-2014 White Paper
This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014
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