34 research outputs found
Physics-Informed Neural Networks for Non-linear System Identification applied to Power System Dynamics
Varying power-infeed from converter-based generation units introduces great
uncertainty on system parameters such as inertia and damping. As a consequence,
system operators face increasing challenges in performing dynamic security
assessment and taking real-time control actions. Exploiting the widespread
deployment of phasor measurement units (PMUs) and aiming at developing a fast
dynamic state and parameter estimation tool, this paper investigates the
performance of Physics-Informed Neural Networks (PINN) for discovering the
frequency dynamics of future power systems and monitoring the system inertia in
real-time. PINNs have the potential to address challenges such as the stronger
non-linearities of low-inertia systems, increased measurement noise, and
limited availability of data. The estimator is demonstrated in several test
cases using a 4-bus system, and compared with state of the art algorithms, such
as the Unscented Kalman Filter (UKF), to assess its performance.Comment: 6 pages, 8 figures, submitted to 59th Conference on Decision and
Contro
Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization
This paper proposes a tractable framework to determine key characteristics of
non-linear dynamic systems by converting physics-informed neural networks to a
mixed integer linear program. Our focus is on power system applications.
Traditional methods in power systems require the use of a large number of
simulations and other heuristics to determine parameters such as the critical
clearing time, i.e. the maximum allowable time within which a disturbance must
be cleared before the system moves to instability. The work proposed in this
paper uses physics-informed neural networks to capture the power system dynamic
behavior and, through an exact transformation, converts them to a tractable
optimization problem which can be used to determine critical system indices. By
converting neural networks to mixed integer linear programs, our framework also
allows to adjust the conservativeness of the neural network output with respect
to the existing stability boundaries. We demonstrate the performance of our
methods on the non-linear dynamics of converter-based generation in response to
voltage disturbances.Comment: 6 pages, 5 figures, submitted to the 60th IEEE conference on Decision
and Control (CDC), 2021, Austin, Texas, US
Zero-inertia Offshore Grids: N-1 Security and Active Power Sharing
With Denmark dedicated to maintaining its leading position in the integration
of massive shares of wind energy, the construction of new offshore energy
islands has been recently approved by the Danish government. These new islands
will be zero-inertia systems, meaning that no synchronous generation will be
installed in the island and that power imbalances will be shared only among
converters. To this end, this paper proposes a methodology to calculate and
update the frequency droops gains of the offshore converters in compliance with
the N-1 security criterion in case of converter outage. The frequency droop
gains are calculated solving an optimization problem which takes into
consideration the power limitations of the converters as well as the stability
of the system. As a consequence, the proposed controller ensures safe operation
of off-shore systems in the event of any power imbalance and allows for greater
loadability at pre-fault state, as confirmed by the simulation results.Comment: Submitted to "IEEE Transactions on Power Systems" on February 19,
202
On the Dynamics of the Deployment of Renewable Energy Production Capacities
This chapter falls within the context of modeling the deployment of renewable en-ergy production capacities in the scope of the energy transition. This problem is addressed from an energy point of view, i.e. the deployment of technologies is seen as an energy investment under the constraint that an initial budget of non-renewable energy is provided. Using the Energy Return on Energy Investment (ERoEI) characteristics of technologies, we propose MODERN, a discrete-time formalization of the deployment of renewable energy production capacities. Be-sides showing the influence of the ERoEI parameter, the model also underlines the potential benefits of designing control strategies for optimizing the deployment of production capacities, and the necessity to increase energy efficiency.Peer reviewe
On machine learning-based techniques for future sustainable and resilient energy systems
Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified
Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow to Mixed-Integer Linear Programs
This paper introduces a framework to capture previously intractable
optimization constraints and transform them to a mixed-integer linear program,
through the use of neural networks. We encode the feasible space of
optimization problems characterized by both tractable and intractable
constraints, e.g. differential equations, to a neural network. Leveraging an
exact mixed-integer reformulation of neural networks, we solve mixed-integer
linear programs that accurately approximate solutions to the originally
intractable non-linear optimization problem. We apply our methods to the AC
optimal power flow problem (AC-OPF), where directly including dynamic security
constraints renders the AC-OPF intractable. Our proposed approach has the
potential to be significantly more scalable than traditional approaches. We
demonstrate our approach for power system operation considering N-1 security
and small-signal stability, showing how it can efficiently obtain cost-optimal
solutions which at the same time satisfy both static and dynamic security
constraints
Actualised and future changes in regional economic growth through sea level rise
This study investigates the long-term economic impact of sea-level rise (SLR)
on coastal regions in Europe, focusing on Gross Domestic Product (GDP). Using a
novel dataset covering regional SLR and economic growth from 1900 to 2020, we
quantify the relationships between SLR and regional GDP per capita across 79
coastal EU & UK regions. Our results reveal that the current SLR has already
negatively influenced GDP of coastal regions, leading to a cumulative 4.7% loss
at 39 cm of SLR. Over the 120 year period studied, the actualised impact of SLR
on the annual growth rate is between -0.02% and 0.04%. Extrapolating these
findings to future climate and socio-economic scenarios, we show that in the
absence of additional adaptation measures, GDP losses by 2100 could range
between -6.3% and -20.8% under the most extreme SLR scenario (SSP5-RCP8.5
High-end Ice, or -4.0% to -14.1% in SSP5-RCP8.5 High Ice). This statistical
analysis utilising a century-long dataset, provides an empirical foundation for
designing region-specific climate adaptation strategies to mitigate economic
damages caused by SLR. Our evidence supports the argument for strategically
relocating assets and establishing coastal setback zones when it is
economically preferable and socially agreeable, given that protection
investments have an economic impact
Systematic sensitivity analysis of the full economic impacts of sea level rise
The potential impacts of sea level rise (SLR) due to climate change have been widely studied in the literature. However, the uncertainty and robustness of these estimates has seldom been explored. Here we assess the model input uncertainty regarding the wide effects of SLR on marine navigation from a global economic perspective. We systematically assess the robustness of computable general equilibrium (CGE) estimates to model’s inputs uncertainty. Monte Carlo (MC) and Gaussian quadrature (GQ) methods are used for conducting a Systematic sensitivity analysis (SSA). This design allows to both explore the sensitivity of the CGE model and to compare the MC and GQ methods. Results show that, regardless whether triangular or piecewise linear Probability distributions are used, the welfare losses are higher in the MC SSA than in the original deterministic simulation. This indicates that the CGE economic literature has potentially underestimated the total economic effects of SLR, thus stressing the necessity of SSA when simulating the general equilibrium effects of SLR. The uncertainty decomposition shows that land losses have a smaller effect compared to capital and seaport productivity losses. Capital losses seem to affect the developed regions GDP more than the productivity losses do. Moreover, we show the uncertainty decomposition of the MC results and discuss the convergence of the MC results for a decomposed version of the CGE model. This paper aims to provide standardised guidelines for stochastic simulation in the context of CGE modelling that could be useful for researchers in similar settings