586 research outputs found
Counterexample to Equivalent Nodal Analysis for Voltage Stability Assessment
Existing literature claims that the L-index for voltage instability detection
is inaccurate and proposes an improved index quantifying voltage stability
through system equivalencing. The proposed stability condition is claimed to be
exact in determining voltage instability.We show the condition is incorrect
through simple arguments accompanied by demonstration on a two-bus system
counterexample.Comment: 3 pages, 3 figure
Energy Disaggregation via Deep Temporal Dictionary Learning
This paper addresses the energy disaggregation problem, i.e. decomposing the
electricity signal of a whole home to its operating devices. First, we cast the
problem as a dictionary learning (DL) problem where the key electricity
patterns representing consumption behaviors are extracted for each device and
stored in a dictionary matrix. The electricity signal of each device is then
modeled by a linear combination of such patterns with sparse coefficients that
determine the contribution of each device in the total electricity. Although
popular, the classic DL approach is prone to high error in real-world
applications including energy disaggregation, as it merely finds linear
dictionaries. Moreover, this method lacks a recurrent structure; thus, it is
unable to leverage the temporal structure of energy signals. Motivated by such
shortcomings, we propose a novel optimization program where the dictionary and
its sparse coefficients are optimized simultaneously with a deep neural model
extracting powerful nonlinear features from the energy signals. A long
short-term memory auto-encoder (LSTM-AE) is proposed with tunable time
dependent states to capture the temporal behavior of energy signals for each
device. We learn the dictionary in the space of temporal features captured by
the LSTM-AE rather than the original space of the energy signals; hence, in
contrast to the traditional DL, here, a nonlinear dictionary is learned using
powerful temporal features extracted from our deep model. Real experiments on
the publicly available Reference Energy Disaggregation Dataset (REDD) show
significant improvement compared to the state-of-the-art methodologies in terms
of the disaggregation accuracy and F-score metrics.Comment: 8 pages, 7 figure
A Necessary Condition for Power Flow Insolvability in Power Distribution Systems with Distributed Generators
This paper proposes a necessary condition for power flow insolvability in
power distribution systems with distributed generators (DGs). We show that the
proposed necessary condition indicates the impending singularity of the
Jacobian matrix and the onset of voltage instability. We consider different
operation modes of DG inverters, e.g., constant-power and constant-current
operations, in the proposed method. A new index based on the presented
necessary condition is developed to indicate the distance between the current
operating point and the power flow solvability boundary. Compared to existing
methods, the operating condition-dependent critical loading factor provided by
the proposed condition is less conservative and is closer to the actual power
flow solution space boundary. The proposed method only requires the present
snapshots of voltage phasors to monitor the power flow insolvability and
voltage stability. Hence, it is computationally efficient and suitable to be
applied to a power distribution system with volatile DG outputs. The accuracy
of the proposed necessary condition and the index is validated by simulations
on a distribution test system with different DG penetration levels
Markov Decision Process-based Resilience Enhancement for Distribution Systems: An Approximate Dynamic Programming Approach
Because failures in distribution systems caused by extreme weather events
directly result in consumers' outages, this paper proposes a state-based
decision-making model with the objective of mitigating loss of load to improve
the distribution system resilience throughout the unfolding events. The
sequentially uncertain system states, e.g., feeder line on/off states, driven
by the unfolding events are modeled as Markov states, and the probabilities
from one Markov state to another Markov state throughout the unfolding events
are determined by the component failure caused by the unfolding events. A
recursive optimization model based on Markov decision processes (MDP) is
developed to make state-based actions, i.e., system reconfiguration, at each
decision time. To overcome the curse of dimensionality caused by enormous
states and actions, an approximate dynamic programming (ADP) approach based on
post-decision states and iteration is used to solve the proposed MDP-based
model. IEEE 33-bus system and IEEE 123-bus system are used to validate the
proposed model
A Learning-based Power Management for Networked Microgrids Under Incomplete Information
This paper presents an approximate Reinforcement Learning (RL) methodology
for bi-level power management of networked Microgrids (MG) in electric
distribution systems. In practice, the cooperative agent can have limited or no
knowledge of the MG asset behavior and detailed models behind the Point of
Common Coupling (PCC). This makes the distribution systems unobservable and
impedes conventional optimization solutions for the constrained MG power
management problem. To tackle this challenge, we have proposed a bi-level RL
framework in a price-based environment. At the higher level, a cooperative
agent performs function approximation to predict the behavior of entities under
incomplete information of MG parametric models; while at the lower level, each
MG provides power-flow-constrained optimal response to price signals. The
function approximation scheme is then used within an adaptive RL framework to
optimize the price signal as the system load and solar generation change over
time. Numerical experiments have verified that, compared to previous works in
the literature, the proposed privacy-preserving learning model has better
adaptability and enhanced computational speed
A Data-Driven Customer Segmentation Strategy Based on Contribution to System Peak Demand
Advanced metering infrastructure (AMI) enables utilities to obtain granular
energy consumption data, which offers a unique opportunity to design customer
segmentation strategies based on their impact on various operational metrics in
distribution grids. However, performing utility-scale segmentation for
unobservable customers with only monthly billing information, remains a
challenging problem. To address this challenge, we propose a new metric, the
coincident monthly peak contribution (CMPC), that quantifies the contribution
of individual customers to system peak demand. Furthermore, a novel multi-state
machine learning-based segmentation method is developed that estimates CMPC for
customers without smart meters (SMs): first, a clustering technique is used to
build a databank containing typical daily load patterns in different seasons
using the SM data of observable customers. Next, to associate unobservable
customers with the discovered typical load profiles, a classification approach
is leveraged to compute the likelihood of daily consumption patterns for
different unobservable households. In the third stage, a weighted clusterwise
regression (WCR) model is utilized to estimate the CMPC of unobservable
customers using their monthly billing data and the outcomes of the
classification module. The proposed segmentation methodology has been tested
and verified using real utility data
Mathematical representation of the WECC composite load model
The Western Electricity Coordinating Council (WECC) composite load model is a
newly developed load model that has drawn great interest from the industry. To
analyze its dynamic characteristics with both mathematical and engineering
rigor, a detailed mathematical model is needed. Although WECC composite load
model is available in commercial software as a module and its detailed block
diagrams can be found in several public reports, there is no complete
mathematical representation of the full model in literature. This paper
addresses a challenging problem of deriving detailed mathematical
representation of WECC composite load model from its block diagrams. In
particular, for the first time, we have derived the mathematical representation
of the new DER_A model. The developed mathematical model is verified using both
Matlab and PSS/E to show its effectiveness in representing WECC composite load
model. The derived mathematical representation serves as an important
foundation for parameter identification, order reduction and other dynamic
analysis
A Data-Driven Framework for Assessing Cold Load Pick-up Demand in Service Restoration
Cold load pick-up (CLPU) has been a critical concern to utilities.
Researchers and industry practitioners have underlined the impact of CLPU on
distribution system design and service restoration. The recent large-scale
deployment of smart meters has provided the industry with a huge amount of data
that is highly granular, both temporally and spatially. In this paper, a
data-driven framework is proposed for assessing CLPU demand of residential
customers using smart meter data. The proposed framework consists of two
interconnected layers: 1) At the feeder level, a nonlinear auto-regression
model is applied to estimate the diversified demand during the system
restoration and calculate the CLPU demand ratio. 2) At the customer level,
Gaussian Mixture Models (GMM) and probabilistic reasoning are used to quantify
the CLPU demand increase. The proposed methodology has been verified using real
smart meter data and outage cases
On the Translation of Folding Beijing from the Perspective of Horizon of Expectations
In 2016, the success of Folding Beijing winning the 74th Hugo Award made Chinese science fiction attract the world literary circle again. Based on the reader’s horizon of expectations, this paper gives a brief analysis of Ken Liu’s translation of Folding Beijing. It also seeks to demonstrate Ken Liu’s consideration for target language readers in the course of translating the Chinese expressions with connotation and the translation methods he adopts to reduce the cultural differences and achieve the reception and comprehension of the translated text among the target audience.
A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
This paper presents a review of the literature on State Estimation (SE) in
power systems. While covering some works related to SE in transmission systems,
the main focus of this paper is Distribution System State Estimation (DSSE).
The paper discusses a few critical topics of DSSE, including mathematical
problem formulation, application of pseudo-measurements, metering instrument
placement, network topology issues, impacts of renewable penetration, and
cyber-security. Both conventional and modern data-driven and probabilistic
techniques have been reviewed. This paper can provide researchers and utility
engineers with insights into the technical achievements, barriers, and future
research directions of DSSE
- …