240,654 research outputs found
Power Losses Estimation in Low Voltage Smart Grids
MenciĂłn Internacional en el tĂtulo de doctorOne of the European Union Targets was to replace at least 80% of all traditional energy
meters with electronic smart meters by 2020. However, by the end of 2020, the European
region (EU 27 including the UK) had installed no more than 150 million smart electricity
meters, representing a penetration rate of 50% for smart meters. By 2026, It is expected
that there will be more than 227 million smart meters in households due to the updated
planning and target numbers, which will affect many European markets, including western
and northern Europe. This scenario would contribute to the general purpose of building
a more sustainable distribution system for the future.
This thesis contributes to the field of power losses estimation and optimization in
low-voltage (LV) smart grids in large-scale distribution areas. To contextualize the importance
of the research, it has been necessary to explain the unbalanced nature of low
voltage distribution networks where there is a huge deployment of smart meter rollout,
and there is also uncertainty related to renewable energy generation. Main results of the
thesis have been applied in two smart grid research projects: the national project OSIRIS
(OptimizaciÂŽon de la SupervisiÂŽon Inteligente de la Red de DistribuciÂŽon) and the European
project IDE4L (Ideal Grid For All ).
Smart metering infrastructure allows distributor system operators (DSOs) to have detailed
information about the customers energy consumption or generation. Smart meters
measure the active and reactive energy consumption/generation of customers using different
discrete time resolutions which range from 15-60 min. A large-scale smart meter
rollout allows service providers to gain information about the energy consumed and produced
by each customer in near-real time. This knowledge can be used to compute the aggregated network power losses at any given time. In this case, network power losses
are calculated by means of customersâ smart meters measurements, in terms of both active
and reactive energy consumption, and by the energy measured by the smart meter
supervisor located at the secondary substation (SS).
The problem of network losses estimation becomes more challenging as a results of
the existence of not-technical losses due to electricity fraud or smart meter measurements
anomalous (null or extremely high) or even because there are customersâ smart meters
that can be out of service.
One of the differential keys of LV smart grids is the presence of single-phase loads
and unbalanced operation, which makes it necessary to adopt a complete three-phase
model of the LV distribution network to calculate the real value of the power losses. This
scenario makes the process of power loss estimation a computationally intensive problem.
The challenge is even greater when estimating the power losses of large-scale distribution
networks, composed of thousands of SSs.
In recent years, environmental concerns have led to the increasing integration of a considerable
number of distributed energy resources (DERs) into LV smart grids. This fact
prompts DSOs and regulators to provide the maximum energy efficiency in their networks
(i.e., the smallest power loss values) and maximum sustainable energy consumption. Detailed
understanding of the networkâs behavior in terms of power losses and the use of
electricity is necessary to achieve this energy efficiency.
However, the above scenario presents some drawbacks. The integration of DERs units,
such as photovoltaic (PV) panels, into distribution networks can produce an increment
of network power losses if the DERs units are not optimally located, coordinated, or controlled.
Additionally, the network can experience technical contingencies such as cableâs
overloads and nodal over-voltages or can lead to an inefficient system operation due to
high energy losses or cables that exceed thermal limits. Moreover, there is a great uncertainty
associated with the distributed power generation from PVs because its energy
generation depend on weather conditions, including ambient temperature and solar irradiance,
which are highly intermittent and fluctuating. Uncertainty is also present in some
loads with stochastic behavior, such as plug-in electric vehicles (PEV), which adds an uncertainty layer and makes their optimal integration more complex.
Therefore, DSOs require advanced methods to estimate power losses in unbalanced
large-scale LV smart grids under uncertain situations. Such estimations would facilitate
the deployment of policies and practices that lead to a safe and efficient integration of
DERs in the form of flexibility mechanisms. In this context, flexibility mechanisms are
essential to achieve optimal operation conditions under extreme uncertainty. Flexibility
mechanisms can be deployed to tackle the imbalance between generation and demand
that results from the uncertainty that is latent in LV smart grids.
These flexibility mechanisms are based on modifying the normal power consumption
(for the demand side) or power generation (for the generation side), according to a flexibility
scheduling at the request of the network operator.
In summary, DSOs face the challenge of managing network losses over large geographical
areas where there are hundreds of secondary substations and thousands of feeders,
with multiple customers and an ever-increasing presence of renewable DERs. Power losses
estimation is thus paramount to improve network energy efficiency in the context of the
European Union energy policies. This situation is complicated by the unbalanced operation
of those networks and the presence of uncertainty. To address these challenges, this
thesis focuses on the following objectives:
1. Power losses estimation in unbalanced LV smart grids under uncertainty.
2. Power losses estimation in unbalanced LV smart grids in large areas with a presence
of DERs.
3. Flexibility scheduling for power losses minimization in unbalanced smart grids under
uncertainty.
The mentioned objectives are achieved by taking advantage of smart metering infrastructures,
machine and deep learning models and mathematical programming techniques
which allows DSOs to reduce their total power losses within the distribution network.
This approach entails using flexibility mechanisms to operate the distribution network
optimally and enhance the load management and DG expansion planning. According to the objectives identified earlier, the main contributions of this thesis are
the following:
1. Power losses estimation in unbalanced LV smart grids under uncertainty conditions.
An optimization-based procedure to estimate load consumption of non-telemetered
customers.
A Markov chain-based process to estimate intra-hour load demand for data
having a low resolution and for non-telemetered customers or customers which
smart meters provide incorrect measurements.
2. Power losses estimation in unbalanced LV smart grids in large-scale areas with a
presence of DERs.
A data mining approach to reduce a high-dimensionality dataset in smart grids
to yield a reduced set of relevant features.
A clustering process to obtain representative feeders within a large-scale distribution
area of smart grids.
A deep learning-based power losses estimator for large-scale LV smart grids.
The method is formulated as a deep neural network that uses as input features
the power load demand and power generation of a set of representative feeders.
The model gives, as output, the power losses of the whole area.
3. Flexibility scheduling for power losses minimization in unbalanced smart grids under
uncertainty.
A robust optimization model for the flexibility scheduling optimization model
for unbalanced smart grids with distributed resources, such as PV panels and
PEV devices.Programa de Doctorado en IngenierĂa ElĂ©ctrica, ElectrĂłnica y AutomĂĄtica por la Universidad Carlos III de MadridPresidenta: Natalia Alguacil Conde.- Secretario: Pablo Ledesma Larrea.- Vocal: Samuele Grill
Distributed Random Convex Programming via Constraints Consensus
This paper discusses distributed approaches for the solution of random convex
programs (RCP). RCPs are convex optimization problems with a (usually large)
number N of randomly extracted constraints; they arise in several applicative
areas, especially in the context of decision under uncertainty, see [2],[3]. We
here consider a setup in which instances of the random constraints (the
scenario) are not held by a single centralized processing unit, but are
distributed among different nodes of a network. Each node "sees" only a small
subset of the constraints, and may communicate with neighbors. The objective is
to make all nodes converge to the same solution as the centralized RCP problem.
To this end, we develop two distributed algorithms that are variants of the
constraints consensus algorithm [4],[5]: the active constraints consensus (ACC)
algorithm, and the vertex constraints consensus (VCC) algorithm. We show that
the ACC algorithm computes the overall optimal solution in finite time, and
with almost surely bounded communication at each iteration. The VCC algorithm
is instead tailored for the special case in which the constraint functions are
convex also w.r.t. the uncertain parameters, and it computes the solution in a
number of iterations bounded by the diameter of the communication graph. We
further devise a variant of the VCC algorithm, namely quantized vertex
constraints consensus (qVCC), to cope with the case in which communication
bandwidth among processors is bounded. We discuss several applications of the
proposed distributed techniques, including estimation, classification, and
random model predictive control, and we present a numerical analysis of the
performance of the proposed methods. As a complementary numerical result, we
show that the parallel computation of the scenario solution using ACC algorithm
significantly outperforms its centralized equivalent
Geometric Interpretation of Theoretical Bounds for RSS-based Source Localization with Uncertain Anchor Positions
The Received Signal Strength based source localization can encounter severe
problems originating from uncertain information about the anchor positions in
practice. The anchor positions, although commonly assumed to be precisely known
prior to the source localization, are usually obtained using previous
estimation algorithm such as GPS. This previous estimation procedure produces
anchor positions with limited accuracy that result in degradations of the
source localization algorithm and topology uncertainty. We have recently
addressed the problem with a joint estimation framework that jointly estimates
the unknown source and uncertain anchors positions and derived the theoretical
limits of the framework. This paper extends the authors previous work on the
theoretical performance bounds of the joint localization framework with
appropriate geometric interpretation of the overall problem exploiting the
properties of semi-definiteness and symmetry of the Fisher Information Matrix
and the Cram{\`e}r-Rao Lower Bound and using Information and Error Ellipses,
respectively. The numerical results aim to illustrate and discuss the
usefulness of the geometric interpretation. They provide in-depth insight into
the geometrical properties of the joint localization problem underlining the
various possibilities for practical design of efficient localization
algorithms.Comment: 30 pages, 15 figure
Distributed Adaptive Fault-Tolerant Control of Uncertain Multi-Agent Systems
This paper presents an adaptive fault-tolerant control (FTC) scheme for a
class of nonlinear uncertain multi-agent systems. A local FTC scheme is
designed for each agent using local measurements and suitable information
exchanged between neighboring agents. Each local FTC scheme consists of a fault
diagnosis module and a reconfigurable controller module comprised of a baseline
controller and two adaptive fault-tolerant controllers activated after fault
detection and after fault isolation, respectively. Under certain assumptions,
the closed-loop system's stability and leader-follower consensus properties are
rigorously established under different modes of the FTC system, including the
time-period before possible fault detection, between fault detection and
possible isolation, and after fault isolation
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Uncertainty modelling in power system state estimation
A method for uncertainty analysis in power system state estimation is proposed. The two-step method uses static weighted least-squares analysis to compute 'point' state estimates. Linear programming is then employed to obtain the upper and lower bounds of the uncertainty interval. It is shown that the method can provide useful additional information for both metered and nonmetered elements of the system. The effects of network parameter errors are also studied. For illustrative purposed, the proposed method is tested using the six-bus and IEEE 30-bus standard systems. Results show that the proposed method is an accurate and reliable tool for estimating the uncertainty bounds in power system state estimation
Measurement network design including traveltime determinations to minimize model prediction uncertainty
Traveltime determinations have found increasing application in the characterization of groundwater systems. No algorithms are available, however, to optimally design sampling strategies including this information type. We propose a first-order methodology to include groundwater age or tracer arrival time determinations in measurement network design and apply the methodology in an illustrative example in which the network design is directed at contaminant breakthrough uncertainty minimization. We calculate linearized covariances between potential measurements and the goal variables of which we want to reduce the uncertainty: the groundwater age at the control plane and the breakthrough locations of the contaminant. We assume the traveltime to be lognormally distributed and therefore logtransform the age determinations in compliance with the adopted Bayesian framework. Accordingly, we derive expressions for the linearized covariances between the transformed age determinations and the parameters and states. In our synthetic numerical example, the derived expressions are shown to provide good first-order predictions of the variance of the natural logarithm of groundwater age if the variance of the natural logarithm of the conductivity is less than 3.0. The calculated covariances can be used to predict the posterior breakthrough variance belonging to a candidate network before samples are taken. A Genetic Algorithm is used to efficiently search, among all candidate networks, for a near-optimal one. We show that, in our numerical example, an age estimation network outperforms (in terms of breakthrough uncertainty reduction) equally sized head measurement networks and conductivity measurement networks even if the age estimations are highly uncertain
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