Rights:
Atribución-NoComercial-SinDerivadas 3.0 España
Abstract:
One 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 electrOne 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
(Optimizaci´on de la Supervisi´on Inteligente de la Red de Distribuci´on) 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 customers’ 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 customers’ 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 network’s 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 cable’s
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.[+][-]