91 research outputs found
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
Interdependency of Transmission and Distribution Pricing
Distribution markets are among the prospect being considered for the future
of power systems. They would facilitate integration of distributed energy
resources (DERs) and microgrids via a market mechanism and enable them to
monetize services they can provide. This paper follows the ongoing work in
implementing the distribution market operator (DMO) concept, and its clearing
and settlement procedures, and focuses on investigating the pricing conducted
by the DMO. The distribution locational marginal prices (D-LMPs) and their
relationship with the transmission system locational marginal prices (T-LMPs)
are subject of this paper. Numerical simulations on a test distribution system
exhibit the benefits and drawbacks of the proposed DMO pricing processes.Comment: Accepted to 2016 IEEE PES Innovative Smart Grid Technologies (ISGT
Distribution market as a ramping aggregator for grid flexibility support
The growing proliferation of microgrids and distributed energy resources in
distribution networks has resulted in the development of Distribution Market
Operator (DMO). This new entity will facilitate the management of the
distributed resources and their interactions with upstream network and the
wholesale market. At the same time, DMOs can tap into the flexibility potential
of these distributed resources to address many of the challenges that system
operators are facing. This paper investigates this opportunity and develops a
distribution market scheduling model based on upstream network ramping
flexibility requirements. That is, the distribution network will play the role
of a flexibility resource in the system, with a relatively large size and
potential, to help bulk system operators to address emerging ramping concerns.
Numerical simulations demonstrate the effectiveness of the proposed model on
when tested on a distribution system with several microgrids.Comment: IEEE PES Transmission and Distribution Conference and Exposition
(T&D), Denver, CO, 16-19 Apr. 201
Capturing Distribution Grid-Integrated Solar Variability and Uncertainty Using Microgrids
The variable nature of the solar generation and the inherent uncertainty in
solar generation forecasts are two challenging issues for utility grids,
especially as the distribution grid integrated solar generation proliferates.
This paper offers to utilize microgrids as local solutions for mitigating these
negative drawbacks and helping the utility grid in hosting a higher penetration
of solar generation. A microgrid optimal scheduling model based on robust
optimization is developed to capture solar generation variability and
uncertainty. Numerical simulations on a test feeder indicate the effectiveness
of the proposed model.Comment: IEEE Power and Energy Society General Meeting, 201
Machine Learning Applications in Estimating Transformer Loss of Life
Transformer life assessment and failure diagnostics have always been
important problems for electric utility companies. Ambient temperature and load
profile are the main factors which affect aging of the transformer insulation,
and consequently, the transformer lifetime. The IEEE Std. C57.911995 provides a
model for calculating the transformer loss of life based on ambient temperature
and transformer's loading. In this paper, this standard is used to develop a
data-driven static model for hourly estimation of the transformer loss of life.
Among various machine learning methods for developing this static model, the
Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical
simulations demonstrate the effectiveness and the accuracy of the proposed
ANFIS method compared with other relevant machine learning based methods to
solve this problem.Comment: IEEE Power and Energy Society General Meeting, 201
Day-Ahead Solar Forecasting Based on Multi-level Solar Measurements
The growing proliferation in solar deployment, especially at distribution
level, has made the case for power system operators to develop more accurate
solar forecasting models. This paper proposes a solar photovoltaic (PV)
generation forecasting model based on multi-level solar measurements and
utilizing a nonlinear autoregressive with exogenous input (NARX) model to
improve the training and achieve better forecasts. The proposed model consists
of four stages of data preparation, establishment of fitting model, model
training, and forecasting. The model is tested under different weather
conditions. Numerical simulations exhibit the acceptable performance of the
model when compared to forecasting results obtained from two-level and
single-level studies
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