Analysis of DC microgrids as stochastic hybrid systems

A modeling framework for dc microgrids and distribution systems based on the dual active bridge (DAB) topology is presented. The purpose of this framework is to accurately characterize dynamic behavior of multi-converter systems as a function of exogenous load and source inputs. The base model is derived for deterministic inputs and then extended for the case of stochastic load behavior. At the core of the modeling framework is a large-signal DAB model that accurately describes the dynamics of both ac and dc state variables. This model addresses limitations of existing DAB converter models, which are not suitable for system-level analysis due to inaccuracy and poor upward scalability. The converter model acts as a fundamental building block in a general procedure for constructing models of multi-converter systems. System-level model construction is only possible due to structural properties of the converter model that mitigate prohibitive increases in size and complexity. To characterize the impact of randomness in practical loads, stochastic load descriptions are included in the deterministic dynamic model. The combined behavior of distributed loads is represented by a continuous-time stochastic process. Models that govern this load process are generated using a new modeling procedure, which builds incrementally from individual device-level representations. To merge the stochastic load process and deterministic dynamic models, the microgrid is modeled as a stochastic hybrid system. The stochastic hybrid model predicts the evolution of moments of dynamic state variables as a function of load model parameters. Moments of dynamic states provide useful approximations of typical system operating conditions over time. Applications of the deterministic models include system stability analysis and computationally efficient time-domain simulation. The stochastic hybrid models provide a framework for performance assessment and optimization --Abstract, page iv

Unsupervised training methods for non-intrusive appliance load monitoring from smart meter data

Non-intrusive appliance load monitoring (NIALM) is the process of disaggregating a household’s total electricity consumption into its contributing appliances. Smart meters are currently being deployed on national scales, providing a platform to collect aggregate household electricity consumption data. Existing approaches to NIALM require a manual training phase in which either sub-metered appliance data is collected or appliance usage is manually labelled. This training data is used to build models of the house- hold appliances, which are subsequently used to disaggregate the household’s electricity data. Due to the requirement of such a training phase, existing approaches do not scale automatically to the national scales of smart meter data currently being collected.In this thesis we propose an unsupervised training method which, unlike existing approaches, does not require a manual training phase. Instead, our approach combines general appliance knowledge with just aggregate smart meter data from the household to perform disaggregation. To do so, we address the following three problems: (i) how to generalise the behaviour of multiple appliances of the same type, (ii) how to tune general knowledge of appliances to the specific appliances within a single household using only smart meter data, and (iii) how to provide actionable energy saving advice based on the tuned appliance knowledge.First, we propose an approach to the appliance generalisation problem, which uses the Tracebase data set to build probabilistic models of household appliances. We take a Bayesian approach to modelling appliances using hidden Markov models, and empirically evaluate the extent to which they generalise to previously unseen appliances through cross validation. We show that learning using multiple appliances vastly outperforms learning from a single appliance by 61–99% when attempting to generalise to a previously unseen appliance, and furthermore that such general models can be learned from only 2–6 appliances.Second, we propose an unsupervised solution to the model tuning problem, which uses only smart meter data to learn the behaviour of the specific appliances in a given house-hold. Our approach uses general appliance models to extract appliance signatures from ?a household’s smart meter data, which are then used to refine the general appliance models. We evaluate the benefit of this process using the Reference Energy Disaggregation Data set, and show that the tuned appliance models more accurately represent the energy consumption behaviour of a given household’s appliances compared to when general appliance models are used, and furthermore that such general models can per- form comparably to when sub-metered data is used for model training. We also show that our tuning approach outperforms the current state of the art, which uses a factorial hidden Markov model to tune the general appliance models.Third, we apply both of these approaches to infer the energy efficiency of refrigerators and freezers in a data set of 117 households. We evaluate the accuracy of our approach, and show that it is able to successfully infer the energy efficiency of combined fridge freezers. We then propose an extension to our model tuning process using factorial hidden semi-Markov models to model households with a separate fridge and freezer. Finally, we show that through this extension our approach is able to simultaneously tune the appliance models of both appliances.The above contributions provide a solution which satisfies the requirements of a NIALM training method which is both unsupervised (no manual interaction required during training) and uses only smart meter data (no installation of additional hardware is required). When combined, the contributions presented in this thesis represent an advancement in the state of the art in the field of non-intrusive appliance load monitoring, and a step towards increasing the efficiency of energy consumption within households

Making Distribution State Estimation Practical: Challenges and Opportunities

In increasingly digitalized and metered distribution networks, state estimation is generally recognized as a key enabler of advanced network management functionalities. However, despite decades of research, the real-life adoption of state estimation in distribution systems remains sporadic. This systematization of knowledge paper discusses the cause for this while comparing industrial and academic experiences and reviewing well- and less-established research directions. We argue that to make distribution system state estimation more practical and applicable in the field, new perspectives are needed. In particular, research should move away from conventional approaches and embrace generalized problem specifications and more comprehensive workflows. These, in turn, require algorithm advancements and more general mathematical formulations. We discuss lines of work to enable the delivery of tangible research.Comment: 10 page

Iterative disaggregation for a class of lumpable discrete-time stochastic automata networks

Cataloged from PDF version of article.Stochastic automata networks (SANs) have been developed and used in the last 15 years as a modeling formalism for large systems that can be decomposed into loosely connected components. In this work, we concentrate on the not so much emphasized discrete-time SANs. First, we remodel and extend an SAN that arises in wireless communications. Second, for an SAN with functional transitions, we derive conditions for a special case of ordinary lumpability in which aggregation is done automaton by automaton. Finally, for this class of lumpable discrete-time SANs we devise an efficient aggregation–iterative disaggregation algorithm and demonstrate its performance on the SAN model of interest. © 2002 Elsevier Science B.V. All rights reserved

Interactive visualization for NILM in large buildings using non-negative matrix factorization

Artículo publicado en abierto mediante APC Elsevier Open AccessNon-intrusive load monitoring (NILM) techniques have recently attracted much interest, since they allow to obtain latent patterns from power demand data in buildings, revealing useful information to the expert user. Unsupervised methods are specially attractive, since they do not require labeled datasets. Particularly, non-negative matrix factorization (NMF) methods decompose a single power demand measurement over a certain time period into a set of components or “parts” that are sparse, non-negative and sum up the original measured quantity. Such components reveal hidden temporal patterns which may be difficult to interpret in complex systems such as large buildings. We suggest to integrate the knowledge of the user into the analysis in order to recognize the real events inside the electric network behind the learnt patterns. In this paper, we integrate the available domain knowledge of the user by means of a visual analytics web application in which an expert user can interact in a fluid way with the NMF outcome through visual approaches such as barcharts, heatmaps or calendars. Our approach is tested with real electric power demand data from a hospital complex, showing how the interpretation of the decomposition is improved by means of interactive data cube visualizations, in which the user can insightfully relate the NMF components to characteristic demand patterns of the hospital such as those derived from human activity, as well as to inefficient behaviors of the largest systems in the hospita

Evaluation of empirical approaches to estimate the variability of erosive inputs in river catchments

Die Dissertation erforscht die Unsicherheit, Sensitivität und Grenzen großskaliger Erosionsmodelle. Die Modellierung basiert auf der allgemeinen Bodenabtragsgleichung (ABAG), Sedimenteintragsverhältnissen (SDR) und europäischen Daten. Für mehrere Regionen Europas wird die Bedeutung der Unsicherheit topographischer Modellparameter, ABAG-Faktoren und kritischer Schwebstofffrachten für die Anwendbarkeit empirischer Modelle zur Beschreibung von Sedimentfrachten und SDR von Flusseinzugsgebieten untersucht. Der Vergleich alternativer Modellparameter sowie Kalibrierungs- und Validierungsdaten zeigt, dass schon grundlegende Modellentscheidungen mit großen Unsicherheiten behaftet sind. Zur Vermeidung falscher Modellvorhersagen sind kalibrierte Modelle genau zu dokumentieren. Auch wenn die geschickte Wahl nicht-topographischer Algorithmen die Modellgüte regionaler Anwendungen verbessern kann, so gibt es nicht die generell beste Lösung. Die Ergebnisse zeigen auch, dass SDR-Modelle stets mit Sedimentfrachten und SDR kalibriert und evaluiert werden sollten. Mit diesem Ansatz werden eine neue europäische Bodenabtragskarte und ein verbessertes SDR-Modell für Einzugsgebiete nördlich der Alpen und in Südosteuropa abgeleitet. In anderen Regionen Europas ist das SDR-Modell bedingt nutzbar. Die Studien zur jährlichen Variabilität der Bodenerosion zeigen, dass jahreszeitlich gewichtete Niederschlagsdaten geeigneter als ungewichtete sind. Trotz zufriedenstellender Modellergebnisse überwinden weder sorgfältige Algorithmenwahl noch Modellverbesserungen die Grenzen europaweiter SDR-Modelle. Diese bestehen aus der Diskrepanz zwischen modellierten Bodenabtrags- und maßgeblich zur beobachteten bzw. kritischen Sedimentfracht beitragenden Prozessen sowie der außergewöhnlich hohen Sedimentmobilisierung durch Hochwässer. Die Integration von nicht von der ABAG beschriebenen Prozessen und von Starkregentagen sowie die Disaggregation kritischer Frachten sollte daher weiter erforscht werden.This dissertation thesis addresses the uncertainty, sensitivity and limitations of large-scale erosion models. The modelling framework consists of the universal soil loss equation (USLE), sediment delivery ratios (SDR) and European data. For several European regions, the relevance of the uncertainty in topographic model parameters, USLE factors and critical yields of suspended solids for the applicability of empirical models to predict sediment yields and SDR of river catchments is systematically evaluated. The comparison of alternative model parameters as well as calibration and validation data shows that even basic modelling decisions are associated with great uncertainties. Consequently, calibrated models have to be well-documented to avoid misapplication. Although careful choices of non-topographic algorithms can also be helpful to improve the model quality in regional applications, there is no definitive universal solution. The results also show that SDR models should always be calibrated and evaluated against sediment yields and SDR. With this approach, a new European soil loss map and an improved SDR model for river catchments north of the Alps and in Southeast Europe are derived. For other parts of Europe, the SDR model is of limited use. The studies on the annual variability of soil erosion reveal that seasonally weighted rainfall data is more appropriate than unweighted data. Despite satisfactory model results, neither the careful algorithm choice nor model improvements overcome the limitations of pan-European SDR models. These limitations are related to the mismatch of modelled soil loss processes and the relevant processes contributing to the observed or critical sediment load as well as the extraordinary sediment mobilisation during floods. Therefore, further research on integrating non-USLE processes and heavy-rainfall data as well as on disaggregating critical yields is needed

Nested Fork-Join Queuing Networks and Their Application to Mobility Airfield Operations Analysis

A single-chain nested fork-join queuing network (FJQN) model of mobility airfield ground processing is proposed. In order to analyze the queuing network model, advances on two fronts are made. First, a general technique for decomposing nested FJQNs with probabilistic forks is proposed, which consists of incorporating feedback loops into the embedded Markov chain of the synchronization station, then using Marie\u27s Method to decompose the network. Numerical studies show this strategy to be effective, with less than two percent relative error in the approximate performance measures in most realistic cases. The second contribution is the identification of a quick, efficient method for solving for the stationary probabilities of the λn/Ck/r/N queue. Unpreconditioned Conjugate Gradient Squared is shown to be the method of choice in the context of decomposition using Marie\u27s Method, thus broadening the class of networks where the method is of practical use. The mobility airfield model is analyzed using the strategies described above, and accurate approximations of airfield performance measures are obtained in a fraction of the time needed for a simulation study. The proposed airfield modeling approach is especially effective for quick-look studies and sensitivity analysis

Non-parametric modeling in non-intrusive load monitoring

Non-intrusive Load Monitoring (NILM) is an approach to the increasingly important task of residential energy analytics. Transparency of energy resources and consumption habits presents opportunities and benefits at all ends of the energy supply-chain, including the end-user. At present, there is no feasible infrastructure available to monitor individual appliances at a large scale. The goal of NILM is to provide appliance monitoring using only the available aggregate data, side-stepping the need for expensive and intrusive monitoring equipment. The present work showcases two self-contained, fully unsupervised NILM solutions: the first featuring non-parametric mixture models, and the second featuring non-parametric factorial Hidden Markov Models with explicit duration distributions. The present implementation makes use of traditional and novel constraints during inference, showing marked improvement in disaggregation accuracy with very little effect on computational cost, relative to the motivating work. To constitute a complete unsupervised solution, labels are applied to the inferred components using a Res-Net-based deep learning architecture. Although this preliminary approach to labelling proves less than satisfactory, it is well-founded and several opportunities for improvement are discussed. Both methods, along with the labelling network, make use of block-filtered data: a steady-state representation that removes transient behaviour and signal noise. A novel filter to achieve this steady-state representation that is both fast and reliable is developed and discussed at length. Finally, an approach to monitor the aggregate for novel events during deployment is developed under the framework of Bayesian surprise. The same non-parametric modelling can be leveraged to examine how the predictive and transitional distributions change given new windows of observations. This framework is also shown to have potential elsewhere, such as in regularizing models against over-fitting, which is an important problem in existing supervised NILM

Otimização aplicada ao monitoramento não intrusivo de cargas elétricas residenciais

Orientador: Marcos Julio Rider FloresDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho apresenta um método de monitoramento não intrusivo (Non-Intrusive Load Monitoring - NILM) baseado em programação linear inteira mista (Mixed-Integer Linear Programming - MILP). NILM são métodos para desagregar leituras de medidores de energia em informações a respeito dos aparelhos eletrodomésticos em operação. Tais informações, como consumo e estado de operação, são valiosas para promover a eficiência energética e manutenção preventiva. A técnica NILM proposta neste trabalho expande o modelo clássico fundamentado em otimização combinatória (Combinatorial Optimization - CO). A nova formulação lida com o problema de ambiguidade de cargas similares, presente no modelo clássico. Restrições lineares são utilizadas para representar eficientemente as assinaturas de carga. Além disso, uma estratégia baseada em janelas temporais é proposta para melhorar o desempenho computacional. A desagregação de cargas pode ser feita utilizando apenas medidas de potência ativa em uma baixa taxa de amostragem, disponível em medidores inteligentes comerciais. A técnica também permite a utilização de outros tipos de medidas, se disponíveis, como a potência reativa. O desempenho do algoritmo é validado utilizando dois casos de teste a partir da base de dados pública AMPds. A taxa de amostragem do caso de teste é de uma amostra por minuto. Os resultados demonstram a habilidade do método proposto para identificar e desagregar com precisão as assinaturas de energia individuais de forma computacionalmente eficienteAbstract: This work presents a non-intrusive load monitoring (NILM) method based on mixed-integer linear programming (MILP). NILM are methods for disaggregating measurements from energy meters into information regarding operating appliances. Such information, such as the power consumption and operating state, are valuable for promoting energy savings and predictive maintenance. The proposed technique expands the classical model based on combinatorial optimization (CO). The new formulation handles the problem of ambiguity of similar loads, present in the classical model. Linear constraints are used to efficiently represent load signatures. Additionally, a window-based strategy is proposed to enhance the computational performance of the proposed NILM algorithm. The disaggregation can be made using only active power measurements at a low sampling rate, which is already available in commercial smart meters. Other features can be added to the model, if available, such as the reactive power. The performance of the algorithm is evaluated using two test cases from the public dataset AMPds. The sampling rate from the test case is of one sample per minute. Results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy signatures in a computationally efficient wayMestradoEnergia ElétricaMestre em Engenharia Elétric