Adjustment of model parameters to estimate distribution transformers remaining lifespan

Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.Fil: Jimenez, Victor Adrian. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Will, Adrian L. E.. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Gotay Sardiñas, Jorge. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentin

Energy Management Strategies in hydrogen Smart-Grids: A laboratory experience

As microgrids gain reputation, nations are making decisions towards a new energetic paradigm where the centralized model is being abandoned in favor of a more sophisticated, reliable, environmentally friendly and decentralized one. The implementation of such sophisticated systems drive to find out new control techniques that make the system “smart”, bringing the Smart-Grid concept. This paper studies the role of Energy Management Strategies (EMSs) in hydrogen microgrids, covering both theoretical and experimental sides. It first describes the commissioning of a new labscale microgrid system to analyze a set of different EMS performance in real-life. This is followed by a summary of the approach used towards obtaining dynamic models to study and refine the different controllers implemented within this work. Then the implementation and validation of the developed EMSs using the new labscale microgrid are discussed. Experimental results are shown comparing the response of simple strategies (hysteresis band) against complex on-line optimization techniques, such as the Model Predictive Control. The difference between both approaches is extensively discussed. Results evidence how different control techniques can greatly influence the plant performance and finally we provide a set of guidelines for designing and operating Smart Grids.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-

Affine arithmetic-based methodology for energy hub operation-scheduling in the presence of data uncertainty

In this study, the role of self-validated computing for solving the energy hub-scheduling problem in the presence of multiple and heterogeneous sources of data uncertainties is explored and a new solution paradigm based on affine arithmetic is conceptualised. The benefits deriving from the application of this methodology are analysed in details, and several numerical results are presented and discussed

Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Stability of microgrids and weak grids with high penetration of variable renewable energy

Autonomous microgrids and weak grids with high penetrations of variable renewable energy (VRE) generation tend to share several common characteristics: i) low synchronous inertia, ii) sensitivity to active power imbalances, and iii) low system strength (as defined by the nodal short circuit ratio). As a result of these characteristics, there is a greater risk of system instability relative to larger grids, especially as the share of VRE is increased. This thesis focuses on the development of techniques and strategies to assess and improve the stability of microgrids and weak grids. In the first part of this thesis, the small-signal stability of inertia-less converter dominated microgrids is analysed, wherein a load flow based method for small-signal model initialisation is proposed and used to examine the effects of topology and network parameters on the stability of the microgrid. The use of a back-to-back dc link to interconnect neighbouring microgrids and provide dynamic frequency support is then proposed to improve frequency stability by helping to alleviate active power imbalances. In the third part of this thesis, a new technique to determine the optimal sizing of smoothing batteries in microgrids is proposed. The technique is based on the temporal variability of the solar irradiance at the specific site location in order to maximise PV penetration without causing grid instability. A technical framework for integrating solar PV plants into weak grids is then proposed, addressing the weaknesses in conventional Grid Codes that fail to consider the unique characteristics of weak grids. Finally, a new technique is proposed for estimating system load relief factors that are used in aggregate single frequency stability models

Grey-box Modelling of a Household Refrigeration Unit Using Time Series Data in Application to Demand Side Management

This paper describes the application of stochastic grey-box modeling to identify electrical power consumption-to-temperature models of a domestic freezer using experimental measurements. The models are formulated using stochastic differential equations (SDEs), estimated by maximum likelihood estimation (MLE), validated through the model residuals analysis and cross-validated to detect model over-fitting. A nonlinear model based on the reversed Carnot cycle is also presented and included in the modeling performance analysis. As an application of the models, we apply model predictive control (MPC) to shift the electricity consumption of a freezer in demand response experiments, thereby addressing the model selection problem also from the application point of view and showing in an experimental context the ability of MPC to exploit the freezer as a demand side resource (DSR).Comment: Submitted to Sustainable Energy Grids and Networks (SEGAN). Accepted for publicatio

Modeling Cascading Failures in Power Systems in the Presence of Uncertain Wind Generation

One of the biggest threats to the power systems as critical infrastructures is large-scale blackouts resulting from cascading failures (CF) in the grid. The ongoing shift in energy portfolio due to ever-increasing penetration of renewable energy sources (RES) may drive the electric grid closer to its operational limits and introduce a large amount of uncertainty coming from their stochastic nature. One worrisome change is the increase in CFs. The CF simulation models in the literature do not allow consideration of RES penetration in studying the grid vulnerability. In this dissertation, we have developed tools and models to evaluate the impact of RE penetration on grid vulnerability to CF. We modeled uncertainty injected from different sources by analyzing actual high-resolution data from North American utilities. Next, we proposed two CF simulation models based on simplified DC power flow and full AC power flow to investigate system behavior under different operating conditions. Simulations show a dramatic improvement in the line flow uncertainty estimation based on the proposed model compared to the simplified DC OPF model. Furthermore, realistic assumptions on the integration of RE resources have been made to enhance our simulation technique. The proposed model is benchmarked against the historical blackout data and widely used models in the literature showing similar statistical patterns of blackout size

Electro-thermal analysis of power converter components in low-voltage DC microgrids for optimal protection system design

Bidirectional power converters are considered to be key elements in interfacing the low voltage dc microgrid with an ac grid. However to date there has been no clear procedure to determine the maximum permissible fault isolation periods of the power converter components against the dc faults. To tackle this problem, this paper presents an electro-thermal analysis of the main elements of a converter: ac inductors, dc capacitors and semiconductors. In doing this, the paper provides a methodology for quantifying fault protection requirements for power converter components in future dc microgrids. The analysis is performed through simulations during normal and fault conditions of a low voltage dc microgrid. The paper develops dynamic electro-thermal models of components based on the design and detailed specification from manufacturer datasheets. The simulations show the impact of different protection system operating speeds on the required converter rating for the studied conditions. This is then translated into actual cost of converter equipment. In this manner, the results can be used to determine the required fault protection operating requirements, coordinated with cost penalties for uprating the converter components

Time-dependent photovoltaic performance assessment on a global scale using artificial neural networks

The integration of Renewable Energy Sources (RESs), particularly solar PhotoVoltaics (PVs) has become an imperative aspect of sustainable energy systems. In this pursuit, accurate and efficient simulation tools play a pivotal role in optimizing the performance of PV systems. Traditional simulation approaches, while effective, are often characterized by computational complexities and time-intensive processes. This paper introduces a groundbreaking paradigm in solar energy modeling by harnessing the power of Artificial Neural Networks (ANNs) to revolutionize the accuracy and reliability of PV system simulations. In this work, an hourly, daily, monthly and yearly comparison of the electrical energy obtained with the 5-parameter model and those obtained with the ANNs was developed. For this purpose, a very wide ensemble of localities around the world and types of PV systems were considered in the training and validation phase. ANNs exhibited a maximum mean absolute relative error of 3.5% during training and consistently maintained hourly relative errors below 5% across diverse localities during validation. Hourly power forecasting remains acceptable also in localities with extreme weather conditions. Monthly errors peak at high negative and positive latitudes in summer months when daylight duration exceeds nighttime. However, in the least accurate locality, yearly energy forecasting yielded a maximum error of 8%. Empirical equations based on the trained ANNs are proposed and a relative input-output importance criterion was applied to detect the impact of air temperature and solar radiation on the performance of each PV module. The proposed ANNs demonstrate significant utility in decision-making and real-time processes, providing a valuable framework for managing energy flows within a network and predicting energy production during specific time intervals. This alternative approach surpasses conventional dynamic simulation methodologies found in existing literature in terms of computational cost with comparable accuracy

The impact of smart grid technology on dielectrics and electrical insulation

Delivery of the Smart Grid is a topic of considerable interest within the power industry in general, and the IEEE specifically. This paper presents the smart grid landscape as seen by the IEEE Dielectrics and Electrical Insulation Society (DEIS) Technical Committee on Smart Grids. We define the various facets of smart grid technology, and present an examination of the impacts on dielectrics within power assets. Based on the trajectory of current research in the field, we identify the implications for asset owners and operators at both the device level and the systems level. The paper concludes by identifying areas of dielectrics and insulation research required to fully realize the smart grid concept. The work of the DEIS is fundamental to achieving the goals of a more active, self-managing grid