6 research outputs found
Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems
This PhD thesis thoroughly examines the utilization of deep learning
techniques as a means to advance the algorithms employed in the monitoring and
optimization of electric power systems. The first major contribution of this
thesis involves the application of graph neural networks to enhance power
system state estimation. The second key aspect of this thesis focuses on
utilizing reinforcement learning for dynamic distribution network
reconfiguration. The effectiveness of the proposed methods is affirmed through
extensive experimentation and simulations.Comment: PhD thesi
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow
(CC-OPF) is an open-source Python code developed for solving economic dispatch
(ED) problem in modern power grids. In recent years, integrating a significant
amount of renewables into a power grid causes high fluctuations and thus brings
a lot of uncertainty to power grid operations. This fact makes the conventional
model-based CC-OPF problem non-convex and computationally complex to solve. The
developed tool presents a novel data-driven approach based on the GP regression
model for solving the CC-OPF problem with a trade-off between complexity and
accuracy. The proposed approach and developed software can help system
operators to effectively perform ED optimization in the presence of large
uncertainties in the power grid.Comment: 6 pages, 2 figure
Move Away from Me! User Repulsion Under Proximity-Induced Interference in OWC Systems
As communication systems shift towards ever higher
frequency bands, the propagation of signal between a user
device and an infrastructure becomes more susceptible to nearby
obstacles including other users. As an extreme case, we consider
such proximity-induced channel impairments in indoor optical
wireless communication (OWC) systems. We set up a model,
where the achievable OWC data rate depends not only on the
relative position between a user device and an infrastructure
access point, but also on the location of other users modeled
as proximal interferers. We use a reinforcement learning (RL)
approach to enable users to find suitable positions, both relative
to the access point and to each other, that maximise the sum-
rate capacity of the system. Our initial results demonstrate a
feasibility of RL-based approach that enables indoor OWC users
to find suitable balance between establishing high-rate direct link
while remaining distant from proximal interferers
Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks
Fifth-Generation (5G) networks have a potential to accelerate power system
transition to a flexible, softwarized, data-driven, and intelligent grid. With
their evolving support for Machine Learning (ML)/Artificial Intelligence (AI)
functions, 5G networks are expected to enable novel data-centric Smart Grid
(SG) services. In this paper, we explore how data-driven SG services could be
integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus
on the State Estimation (SE) function as a key element of the energy management
system and focus on two main questions. Firstly, in a tutorial fashion, we
present an overview on how distributed SE can be integrated with the elements
of the 5G core network and radio access network architecture. Secondly, we
present and compare two powerful distributed SE methods based on: i) graphical
models and belief propagation, and ii) graph neural networks. We discuss their
performance and capability to support a near real-time distributed SE via 5G
network, taking into account communication delays
A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems
The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The future power systems are thus expected to face increased control complexity and challenges pertaining to frequency stability due to lower levels of inertia and damping. As a result, the frequency control and development of novel ancillary services is becoming imperative. This paper proposes a data-driven control scheme, based on Reinforcement Learning (RL), for grid-forming Voltage Source Converters (VSCs), with the goal of exploiting their fast response capabilities to provide fast frequency control to the system. A centralized RL-based controller collects generator frequencies and adjusts the VSC power output, in response to a disturbance, to prevent frequency threshold violations. The proposed control scheme is analyzed and its performance evaluated through detailed time-domain simulations of the IEEE 14-bus test system