RT Dissertation/Thesis
T1 Power Losses Estimation in Low Voltage Smart Grids
A1 Velasco Rodríguez, José Ángel
AB One of the European Union Targets was to replace at least 80% of all traditional energymeters with electronic smart meters by 2020. However, by the end of 2020, the Europeanregion (EU 27 including the UK) had installed no more than 150 million smart electricitymeters, representing a penetration rate of 50% for smart meters. By 2026, It is expectedthat there will be more than 227 million smart meters in households due to the updatedplanning and target numbers, which will affect many European markets, including westernand northern Europe. This scenario would contribute to the general purpose of buildinga more sustainable distribution system for the future.This thesis contributes to the field of power losses estimation and optimization inlow-voltage (LV) smart grids in large-scale distribution areas. To contextualize the importanceof the research, it has been necessary to explain the unbalanced nature of lowvoltage 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 thethesis 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 Europeanproject IDE4L (Ideal Grid For All ).Smart metering infrastructure allows distributor system operators (DSOs) to have detailedinformation about the customers energy consumption or generation. Smart metersmeasure the active and reactive energy consumption/generation of customers using differentdiscrete time resolutions which range from 15-60 min. A large-scale smart meterrollout allows service providers to gain information about the energy consumed and producedby 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 lossesare calculated by means of customers’ smart meters measurements, in terms of both activeand reactive energy consumption, and by the energy measured by the smart metersupervisor located at the secondary substation (SS).The problem of network losses estimation becomes more challenging as a results ofthe existence of not-technical losses due to electricity fraud or smart meter measurementsanomalous (null or extremely high) or even because there are customers’ smart metersthat can be out of service.One of the differential keys of LV smart grids is the presence of single-phase loadsand unbalanced operation, which makes it necessary to adopt a complete three-phasemodel of the LV distribution network to calculate the real value of the power losses. Thisscenario makes the process of power loss estimation a computationally intensive problem.The challenge is even greater when estimating the power losses of large-scale distributionnetworks, composed of thousands of SSs.In recent years, environmental concerns have led to the increasing integration of a considerablenumber of distributed energy resources (DERs) into LV smart grids. This factprompts DSOs and regulators to provide the maximum energy efficiency in their networks(i.e., the smallest power loss values) and maximum sustainable energy consumption. Detailedunderstanding of the network’s behavior in terms of power losses and the use ofelectricity 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 incrementof network power losses if the DERs units are not optimally located, coordinated, or controlled.Additionally, the network can experience technical contingencies such as cable’soverloads and nodal over-voltages or can lead to an inefficient system operation due tohigh energy losses or cables that exceed thermal limits. Moreover, there is a great uncertaintyassociated with the distributed power generation from PVs because its energygeneration depend on weather conditions, including ambient temperature and solar irradiance,which are highly intermittent and fluctuating. Uncertainty is also present in someloads 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 unbalancedlarge-scale LV smart grids under uncertain situations. Such estimations would facilitatethe deployment of policies and practices that lead to a safe and efficient integration ofDERs in the form of flexibility mechanisms. In this context, flexibility mechanisms areessential to achieve optimal operation conditions under extreme uncertainty. Flexibilitymechanisms can be deployed to tackle the imbalance between generation and demandthat 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 flexibilityscheduling at the request of the network operator.In summary, DSOs face the challenge of managing network losses over large geographicalareas where there are hundreds of secondary substations and thousands of feeders,with multiple customers and an ever-increasing presence of renewable DERs. Power lossesestimation is thus paramount to improve network energy efficiency in the context of theEuropean Union energy policies. This situation is complicated by the unbalanced operationof those networks and the presence of uncertainty. To address these challenges, thisthesis 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 presenceof DERs.3. Flexibility scheduling for power losses minimization in unbalanced smart grids underuncertainty.The mentioned objectives are achieved by taking advantage of smart metering infrastructures,machine and deep learning models and mathematical programming techniqueswhich allows DSOs to reduce their total power losses within the distribution network.This approach entails using flexibility mechanisms to operate the distribution networkoptimally and enhance the load management and DG expansion planning. According to the objectives identified earlier, the main contributions of this thesis arethe following:1. Power losses estimation in unbalanced LV smart grids under uncertainty conditions.An optimization-based procedure to estimate load consumption of non-telemeteredcustomers.A Markov chain-based process to estimate intra-hour load demand for datahaving a low resolution and for non-telemetered customers or customers whichsmart meters provide incorrect measurements.2. Power losses estimation in unbalanced LV smart grids in large-scale areas with apresence of DERs.A data mining approach to reduce a high-dimensionality dataset in smart gridsto yield a reduced set of relevant features.A clustering process to obtain representative feeders within a large-scale distributionarea 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 featuresthe 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 underuncertainty.A robust optimization model for the flexibility scheduling optimization modelfor unbalanced smart grids with distributed resources, such as PV panels andPEV devices.
YR 2022
FD 2022-03
LK https://hdl.handle.net/10016/35860
UL https://hdl.handle.net/10016/35860
LA eng
NO Mención Internacional en el título de doctor
DS e-Archivo
RD 13 jun. 2024