4,480 research outputs found
Optimal Ensemble Control of Loads in Distribution Grids with Network Constraints
Flexible loads, e.g. thermostatically controlled loads (TCLs), are
technically feasible to participate in demand response (DR) programs. On the
other hand, there is a number of challenges that need to be resolved before it
can be implemented in practice en masse. First, individual TCLs must be
aggregated and operated in sync to scale DR benefits. Second, the uncertainty
of TCLs needs to be accounted for. Third, exercising the flexibility of TCLs
needs to be coordinated with distribution system operations to avoid
unnecessary power losses and compliance with power flow and voltage limits.
This paper addresses these challenges. We propose a network-constrained,
open-loop, stochastic optimal control formulation. The first part of this
formulation represents ensembles of collocated TCLs modelled by an aggregated
Markov Process (MP), where each MP state is associated with a given power
consumption or production level. The second part extends MPs to a multi-period
distribution power flow optimization. In this optimization, the control of TCL
ensembles is regulated by transition probability matrices and physically
enabled by local active and reactive power controls at TCL locations. The
optimization is solved with a Spatio-Temporal Dual Decomposition (ST-D2)
algorithm. The performance of the proposed formulation and algorithm is
demonstrated on the IEEE 33-bus distribution model.Comment: 7 pages, 6 figures, accepted PSCC 201
Cascading failures in spatially-embedded random networks
Cascading failures constitute an important vulnerability of interconnected
systems. Here we focus on the study of such failures on networks in which the
connectivity of nodes is constrained by geographical distance. Specifically, we
use random geometric graphs as representative examples of such spatial
networks, and study the properties of cascading failures on them in the
presence of distributed flow. The key finding of this study is that the process
of cascading failures is non-self-averaging on spatial networks, and thus,
aggregate inferences made from analyzing an ensemble of such networks lead to
incorrect conclusions when applied to a single network, no matter how large the
network is. We demonstrate that this lack of self-averaging disappears with the
introduction of a small fraction of long-range links into the network. We
simulate the well studied preemptive node removal strategy for cascade
mitigation and show that it is largely ineffective in the case of spatial
networks. We introduce an altruistic strategy designed to limit the loss of
network nodes in the event of a cascade triggering failure and show that it
performs better than the preemptive strategy. Finally, we consider a real-world
spatial network viz. a European power transmission network and validate that
our findings from the study of random geometric graphs are also borne out by
simulations of cascading failures on the empirical network.Comment: 13 pages, 15 figure
Message Passing for Integrating and Assessing Renewable Generation in a Redundant Power Grid
A simplified model of a redundant power grid is used to study integration of
fluctuating renewable generation. The grid consists of large number of
generator and consumer nodes. The net power consumption is determined by the
difference between the gross consumption and the level of renewable generation.
The gross consumption is drawn from a narrow distribution representing the
predictability of aggregated loads, and we consider two different distributions
representing wind and solar resources. Each generator is connected to D
consumers, and redundancy is built in by connecting R of these consumers to
other generators. The lines are switchable so that at any instance each
consumer is connected to a single generator. We explore the capacity of the
renewable generation by determining the level of "firm" generation capacity
that can be displaced for different levels of redundancy R. We also develop
message-passing control algorithm for finding switch settings where no
generator is overloaded.Comment: 10 pages, accepted for HICSS-4
Smart Finite State Devices: A Modeling Framework for Demand Response Technologies
We introduce and analyze Markov Decision Process (MDP) machines to model
individual devices which are expected to participate in future demand-response
markets on distribution grids. We differentiate devices into the following four
types: (a) optional loads that can be shed, e.g. light dimming; (b) deferrable
loads that can be delayed, e.g. dishwashers; (c) controllable loads with
inertia, e.g. thermostatically-controlled loads, whose task is to maintain an
auxiliary characteristic (temperature) within pre-defined margins; and (d)
storage devices that can alternate between charging and generating. Our
analysis of the devices seeks to find their optimal price-taking control
strategy under a given stochastic model of the distribution market.Comment: 8 pages, 8 figures, submitted IEEE CDC 201
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward
Advancements in digital automation for smart grids have led to the
installation of measurement devices like phasor measurement units (PMUs),
micro-PMUs (-PMUs), and smart meters. However, a large amount of data
collected by these devices brings several challenges as control room operators
need to use this data with models to make confident decisions for reliable and
resilient operation of the cyber-power systems. Machine-learning (ML) based
tools can provide a reliable interpretation of the deluge of data obtained from
the field. For the decision-makers to ensure reliable network operation under
all operating conditions, these tools need to identify solutions that are
feasible and satisfy the system constraints, while being efficient,
trustworthy, and interpretable. This resulted in the increasing popularity of
physics-informed machine learning (PIML) approaches, as these methods overcome
challenges that model-based or data-driven ML methods face in silos. This work
aims at the following: a) review existing strategies and techniques for
incorporating underlying physical principles of the power grid into different
types of ML approaches (supervised/semi-supervised learning, unsupervised
learning, and reinforcement learning (RL)); b) explore the existing works on
PIML methods for anomaly detection, classification, localization, and
mitigation in power transmission and distribution systems, c) discuss
improvements in existing methods through consideration of potential challenges
while also addressing the limitations to make them suitable for real-world
applications
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