102 research outputs found
Convex Relaxations and Approximations of Chance-Constrained AC-OPF Problems
This paper deals with the impact of linear approximations for the unknown
nonconvex confidence region of chance-constrained AC optimal power flow
problems. Such approximations are required for the formulation of tractable
chance constraints. In this context, we introduce the first formulation of a
chance-constrained second-order cone (SOC) OPF. The proposed formulation
provides convergence guarantees due to its convexity, while it demonstrates
high computational efficiency. Combined with an AC feasibility recovery, it is
able to identify better solutions than chance-constrained nonconvex AC-OPF
formulations. To the best of our knowledge, this paper is the first to perform
a rigorous analysis of the AC feasibility recovery procedures for robust
SOC-OPF problems. We identify the issues that arise from the linear
approximations, and by using a reformulation of the quadratic chance
constraints, we introduce new parameters able to reshape the approximation of
the confidence region. We demonstrate our method on the IEEE 118-bus system
Towards Electronics-based Emergency Control in Power Grids with High Renewable Penetration
Traditional emergency control schemes in power systems usually accompany with
power interruption yielding severely economic damages to customers. This paper
sketches the ideas of a viable alternative for traditional remedial controls
for power grids with high penetration of renewables, in which the renewables
are integrated with synchronverters to mimic the dynamics of conventional
generators. In this novel emergency control scheme, the power electronics
resources are exploited to control the inertia and damping of the imitated
generators in order to quickly compensate for the deviations caused by fault
and thereby bound the fault-on dynamics and stabilize the power system under
emergency situations. This emergency control not only saves investments and
operating costs for modern and future power systems, but also helps to offer
seamless electricity service to customers. Simple numerical simulation will be
used to illustrate the concept of this paper.Comment: arXiv admin note: text overlap with arXiv:1504.0468
Extended mathematical derivations for the decentralized loss minimization algorithm with the use of inverters
This document contains extended mathematical derivations for the
communication- and model-free loss minimization algorithm. The algorithm is
applied in the distribution grids and exploits the capabilities of the
inverters to control the reactive power output
Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow
Physics-informed neural networks exploit the existing models of the
underlying physical systems to generate higher accuracy results with fewer
data. Such approaches can help drastically reduce the computation time and
generate a good estimate of computationally intensive processes in power
systems, such as dynamic security assessment or optimal power flow. Combined
with the extraction of worst-case guarantees for the neural network
performance, such neural networks can be applied in safety-critical
applications in power systems and build a high level of trust among power
system operators. This paper takes the first step and applies, for the first
time to our knowledge, Physics-Informed Neural Networks with Worst-Case
Guarantees for the DC Optimal Power Flow problem. We look for guarantees
related to (i) maximum constraint violations, (ii) maximum distance between
predicted and optimal decision variables, and (iii) maximum sub-optimality in
the entire input domain. In a range of PGLib-OPF networks, we demonstrate how
physics-informed neural networks can be supplied with worst-case guarantees and
how they can lead to reduced worst-case violations compared with conventional
neural networks.Comment: The code to reproduce all simulation results is available online in
https://github.com/RahulNellikkath/Physics-Informed-Neural-Network-for-DC-OP
Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications
This paper presents for the first time, to our knowledge, a framework for
verifying neural network behavior in power system applications. Up to this
moment, neural networks have been applied in power systems as a black-box; this
has presented a major barrier for their adoption in practice. Developing a
rigorous framework based on mixed integer linear programming, our methods can
determine the range of inputs that neural networks classify as safe or unsafe,
and are able to systematically identify adversarial examples. Such methods have
the potential to build the missing trust of power system operators on neural
networks, and unlock a series of new applications in power systems. This paper
presents the framework, methods to assess and improve neural network robustness
in power systems, and addresses concerns related to scalability and accuracy.
We demonstrate our methods on the IEEE 9-bus, 14-bus, and 162-bus systems,
treating both N-1 security and small-signal stability.Comment: published in IEEE Transactions on Smart Grid
(https://ieeexplore.ieee.org/abstract/document/9141308
Inexact Convex Relaxations for AC Optimal Power Flow: Towards AC Feasibility
Convex relaxations of AC optimal power flow (AC-OPF) problems have attracted
significant interest as in several instances they provably yield the global
optimum to the original non-convex problem. If, however, the relaxation is
inexact, the obtained solution is not AC-feasible. The quality of the obtained
solution is essential for several practical applications of AC-OPF, but
detailed analyses are lacking in existing literature. This paper aims to cover
this gap. We provide an in-depth investigation of the solution characteristics
when convex relaxations are inexact, we assess the most promising AC
feasibility recovery methods for large-scale systems, and we propose two new
metrics that lead to a better understanding of the quality of the identified
solutions. We perform a comprehensive assessment on 96 different test cases,
ranging from 14 to 3120 buses, and we show the following: (i) Despite an
optimality gap of less than 1%, several test cases still exhibit substantial
distances to both AC feasibility and local optimality and the newly proposed
metrics characterize these deviations. (ii) Penalization methods fail to
recover an AC-feasible solution in 15 out of 45 cases, and using the proposed
metrics, we show that most failed test instances exhibit substantial distances
to both AC-feasibility and local optimality. For failed test instances with
small distances, we show how our proposed metrics inform a fine-tuning of
penalty weights to obtain AC-feasible solutions. (iii) The computational
benefits of warm-starting non-convex solvers have significant variation, but a
computational speedup exists in over 75% of the cases
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