Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

Smart Power Grid Synchronization With Fault Tolerant Nonlinear Estimation

Effective real-time state estimation is essential for smart grid synchronization, as electricity demand continues to grow, and renewable energy resources increase their penetration into the grid. In order to provide a more reliable state estimation technique to address the problem of bad data in the PMU-based power synchronization, this paper presents a novel nonlinear estimation framework to dynamically track frequency, voltage magnitudes and phase angles. Instead of directly analyzing in abc coordinate frame, symmetrical component transformation is employed to separate the positive, negative, and zero sequence networks. Then, Clarke\u27s transformation is used to transform the sequence networks into the αβ stationary coordinate frame, which leads to system model formulation. A novel fault tolerant extended Kalman filter based real-time estimation framework is proposed for smart grid synchronization with noisy bad data measurements. Computer simulation studies have demonstrated that the proposed fault tolerant extended Kalman filter (FTEKF) provides more accurate voltage synchronization results than the extended Kalman filter (EKF). The proposed approach has been implemented with dSPACE DS1103 and National Instruments CompactRIO hardware platforms. Computer simulation and hardware instrumentation results have shown the potential applications of FTEKF in smart grid synchronization

Power Losses Estimation in Low Voltage Smart Grids

Mención Internacional en el título 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 (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.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Natalia Alguacil Conde.- Secretario: Pablo Ledesma Larrea.- Vocal: Samuele Grill

Innovative Smart Grid Solutions for Network Planning and Access

Smart Grids are the cornerstone for Distribution System Operators transformation. Having new solutions to deal with historical and future problems is key to ensure a smooth transition to an advanced power system that not only integrate a large share of renewables and distributed energy resources (e.g. storage, electrical vehicles), but also requires efficient operation, better planning and exceptional customer service. EDP Distribuição is at the forefront of this transformation, as it is developing Inovgrid, a smart grid project in Évora city (Portugal), where a smart grid infrastructure was deployed, and new data is now available to incorporate in planning and access tools and procedures, hence contributing to a Smarter Grid. This paper discusses the results that EDP Distribuição has attained so far in these areas of the smart grid development, as well as the projected evolution of these innovative approaches to the future of the distribution grid, which are being developed in European projects like SuSTAINABLE (www.sustainableproject.eu)

Correlation Based Method for Phase Identification in a Three Phase LV Distribution Network

Low voltage distribution networks feature a high degree of load unbalance and the addition of rooftop photovoltaic is driving further unbalances in the network. Single phase consumers are distributed across the phases but even if the consumer distribution was well balanced when the network was constructed changes will occur over time. Distribution transformer losses are increased by unbalanced loadings. The estimation of transformer losses is a necessary part of the routine upgrading and replacement of transformers and the identification of the phase connections of households allows a precise estimation of the phase loadings and total transformer loss. This paper presents a new technique and preliminary test results for a method of automatically identifying the phase of each customer by correlating voltage information from the utility's transformer system with voltage information from customer smart meters. The techniques are novel as they are purely based upon a time series of electrical voltage measurements taken at the household and at the distribution transformer. Experimental results using a combination of electrical power and current of the real smart meter datasets demonstrate the performance of our techniques