A Generic Model of Execution for Synthesizing Domain-Specific Models

Software engineering researchers are challenged to provide increasingly more pow- erful levels of abstractions to address the rising complexity inherent in software solu- tions. One new development paradigm that places models as abstraction at the fore- front of the development process is Model-Driven Software Development (MDSD). MDSD considers models as first class artifacts, extending the capability for engineers to use concepts from the problem domain of discourse to specify apropos solutions. A key component in MDSD is domain-specific modeling languages (DSMLs) which are languages with focused expressiveness, targeting a specific taxonomy of problems. The de facto approach used is to first transform DSML models to an intermediate artifact in a HLL e.g., Java or C++, then execute that resulting code. Our research group has developed a class of DSMLs, referred to as interpreted DSMLs (i-DSMLs), where models are directly interpreted by a specialized execution engine with semantics based on model changes at runtime. This execution engine uses a layered architecture and is referred to as a domain-specific virtual machine (DSVM). As the domain-specific model being executed descends the layers of the DSVM the semantic gap between the user-defined model and the services being provided by the underlying infrastructure is closed. The focus of this research is the synthesis engine, the layer in the DSVM which transforms i-DSML models into executable scripts for the next lower layer to process. The appeal of an i-DSML is constrained as it possesses unique semantics contained within the DSVM. Existing DSVMs for i-DSMLs exhibit tight coupling between the implicit model of execution and the semantics of the domain, making it difficult to develop DSVMs for new i-DSMLs without a significant investment in resources. At the onset of this research only one i-DSML had been created for the user- centric communication domain using the aforementioned approach. This i-DSML is the Communication Modeling Language (CML) and its DSVM is the Communication Virtual machine (CVM). A major problem with the CVM’s synthesis engine is that the domain-specific knowledge (DSK) and the model of execution (MoE) are tightly interwoven consequently subsequent DSVMs would need to be developed from inception with no reuse of expertise. This dissertation investigates how to decouple the DSK from the MoE and sub- sequently producing a generic model of execution (GMoE) from the remaining appli- cation logic. This GMoE can be reused to instantiate synthesis engines for DSVMs in other domains. The generalized approach to developing the model synthesis com- ponent of i-DSML interpreters utilizes a reusable framework loosely coupled to DSK as swappable framework extensions. This approach involves first creating an i-DSML and its DSVM for a second do- main, demand-side smartgrid, or microgrid energy management, and designing the synthesis engine so that the DSK and MoE are easily decoupled. To validate the utility of the approach, the SEs are instantiated using the GMoE and DSKs of the two aforementioned domains and an empirical study to support our claim of reduced developmental effort is performed

Microgrids/Nanogrids Implementation, Planning, and Operation

Today’s power system is facing the challenges of increasing global demand for electricity, high-reliability requirements, the need for clean energy and environmental protection, and planning restrictions. To move towards a green and smart electric power system, centralized generation facilities are being transformed into smaller and more distributed ones. As a result, the microgrid concept is emerging, where a microgrid can operate as a single controllable system and can be viewed as a group of distributed energy loads and resources, which can include many renewable energy sources and energy storage systems. The energy management of a large number of distributed energy resources is required for the reliable operation of the microgrid. Microgrids and nanogrids can allow for better integration of distributed energy storage capacity and renewable energy sources into the power grid, therefore increasing its efficiency and resilience to natural and technical disruptive events. Microgrid networking with optimal energy management will lead to a sort of smart grid with numerous benefits such as reduced cost and enhanced reliability and resiliency. They include small-scale renewable energy harvesters and fixed energy storage units typically installed in commercial and residential buildings. In this challenging context, the objective of this book is to address and disseminate state-of-the-art research and development results on the implementation, planning, and operation of microgrids/nanogrids, where energy management is one of the core issues

Toward General Principles for Resilience Engineering

Maintaining the performance of infrastructure‐dependent systems in the face of surprises and unknowable risks is a grand challenge. Addressing this issue requires a better understanding of enabling conditions or principles that promote system resilience in a universal way. In this study, a set of such principles is interpreted as a group of interrelated conditions or organizational qualities that, taken together, engender system resilience. The field of resilience engineering identifies basic system or organizational qualities (e.g., abilities for learning) that are associated with enhanced general resilience and has packaged them into a set of principles that should be fostered. However, supporting conditions that give rise to such first‐order system qualities remain elusive in the field. An integrative understanding of how such conditions co‐occur and fit together to bring about resilience, therefore, has been less clear. This article contributes to addressing this gap by identifying a potentially more comprehensive set of principles for building general resilience in infrastructure‐dependent systems. In approaching this aim, we organize scattered notions from across the literature. To reflect the partly self‐organizing nature of infrastructure‐dependent systems, we compare and synthesize two lines of research on resilience: resilience engineering and social‐ecological system resilience. Although some of the principles discussed within the two fields overlap, there are some nuanced differences. By comparing and synthesizing the knowledge developed in them, we recommend an updated set of resilience‐enhancing principles for infrastructure‐dependent systems. In addition to proposing an expanded list of principles, we illustrate how these principles can co‐occur and their interdependencies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156462/2/risa13494_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156462/1/risa13494.pd

Exploring the Adoption of Service-Dominant Logic as an Integrative Framework for Assessing Energy Transitions

Energy transitions (ETs) can solve some societal problems but must transform societies. Accordingly, socio-technical transitions and other systemic frameworks have been used to assess ETs. However, based on these frameworks, assessments miss a value co-creation orientation, the focus on actors' researched benefits and enabled service exchange, and the consideration of needed de/re-institutionalization practices. Analyzing those elements could prevent socioeconomic shocks and loss of opportunities and unfold possible ET challenges against ET viability and sustainability. Intending to develop a theory synthesis work for enriching previous frameworks, we propose service-dominant logic (S-D logic) as an integrative framework to assess ETs. We offer a literature review on ET systems' frameworks to compare them with the proposal. We also identify the implications of adopting S-D logic for rethinking energy systems' dynamics and ETs. Thus, we contribute to the literature by providing an integrative framework for assessing ETs and we illustrate its potentialities by deriving some challenges of the current Italian ET. This study paves the way for deeper analyses on the contribution of S-D logic to ETs and the operationalization of other systems' frameworks in our integrative one. Merging with quantitative models could also follow

A Middleware to Support Services Delivery in a Domain-Specific Virtual Machine

The increasing use of model-driven software development has renewed emphasis on using domain-specific models during application development. More specifically, there has been emphasis on using domain-specific modeling languages (DSMLs) to capture user-specified requirements when creating applications. The current approach to realizing these applications is to translate DSML models into source code using several model-to-model and model-to-code transformations. This approach is still dependent on the underlying source code representation and only raises the level of abstraction during development. Experience has shown that developers will many times be required to manually modify the generated source code, which can be error-prone and time consuming. An alternative to the aforementioned approach involves using an interpreted domain-specific modeling language (i-DSML) whose models can be directly executed using a Domain Specific Virtual Machine (DSVM). Direct execution of i-DSML models require a semantically rich platform that reduces the gap between the application models and the underlying services required to realize the application. One layer in this platform is the domain-specific middleware that is responsible for the management and delivery of services in the specific domain. In this dissertation, we investigated the problem of designing the domain-specific middleware of the DSVM to facilitate the bifurcation of the semantics of the domain and the model of execution (MoE) while supporting runtime adaptation and validation. We approached our investigation by seeking solutions to the following sub-problems: (1) How can the domain-specific knowledge (DSK) semantics be separated from the MoE for a given domain? (2) How do we define a generic model of execution (GMoE) of the middleware so that it is adaptable and realizes DSK operations to support delivery of services? (3) How do we validate the realization of DSK operations at runtime? Our research into the domain-specific middleware was done using an i-DSML for the user-centric communication domain, Communication Modeling Language (CML), and for microgrid energy management domain, Microgrid Modeling Language (MGridML). We have successfully developed a methodology to separate the DSK and GMoE of the middleware of a DSVM that supports specialization for a given domain, and is able to perform adaptation and validation at runtime

Controle coordenado em microrredes de baixa tensão baseado no algoritmo power-based control e conversor utility interface

Orientadores: José Antenor Pomilio, Fernando Pinhabel MarafãoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Esta tese apresenta uma possível arquitetura e sua respectiva estratégia de controle para microrredes de baixa tensão, considerando-se a existência de geradores distribuídos pela rede. A técnica explora totalmente a capacidade dos geradores distribuídos em ambos os modos de operação: conectado à rede e ilhado. Quando conectado à rede, sob o modo de otimização global, o controle busca a operação quase ótima da microrrede, reduzindo as perdas de distribuição e os desvios de tensão. Quando em modo ilhado, a técnica regula de forma eficaz os geradores distribuídos disponíveis, garantindo a operação autônoma, segura e suave da microrrede. A estratégia de controle é aplicada a uma estrutura de microrrede completamente despachável, baseada em uma arquitetura de controle mestre-escravo, em que as unidades distribuídas são coordenadas por meio do recém-desenvolvido algoritmo Power-Based Control. As principais vantagens da arquitetura proposta são a expansividade e a capacidade de operar sem sincronização ou sem conhecimento das impedâncias de linha. Além disso, a microrrede regula as interações com a rede por meio do conversor chamado de Utility Interface, o qual é um inversor trifásico com armazenador de energia. Esta estrutura de microrrede permite algumas vantagens como: compensação de desbalanço e reativo, rápida resposta aos transitórios de carga e de rede, e suave transição entre os modos de operação. Em contrapartida, para compartilhar a potência ativa e reativa proporcionalmente entre as unidades distribuídas, controlar a circulação de reativos, e maximizar a operação, a comunicação da microrrede requer em um canal de comunicação confiável, ainda que sem grandes exigências em termos de resolução ou velocidade de transmissão. Neste sentido, foi demonstrado que uma falha na comunicação não colapsa o sistema, apenas prejudica o modo de otimização global. Entretanto, o sistema continua a operar corretamente sob o modo de otimização local, que é baseado em um algoritmo de programação linear que visa otimizar a compensação de reativos, harmônicos e desbalanço de cargas por meio dos gerador distribuído, particularmente, quando sua capacidade de potência é limitada. Esta formulação consiste em atingir melhores índices de qualidade de energia, definidos pelo lado da rede e dentro de uma região factível em termos de capacidade do conversor. Baseado nas medições de tensão e corrente de carga e uma determinada função objetiva, o algoritmo rastreia as correntes da rede ótima, as quais são utilizadas para calcular os coeficientes escalares e finalmente estes são aplicados para encontrar as referências da corrente de compensação. Finalmente, ainda é proposta uma técnica eficiente para controlar os conversores monofásicos conectados arbitrariamente ao sistema de distribuição trifásico, sejam conectados entre fase e neutro ou entre fase e fase, com o objetivo de compensar o desbalanço de carga e controlar o fluxo de potência entre as diferentes fases da microrrede. Isto melhora a qualidade da energia elétrica no ponto de acoplamento comum, melhora o perfil de tensão nas linhas, e reduz as perdas de distribuição. A arquitetura da microrrede e a estratégia de controle foi analisada e validada através de simulações computacionais e resultados experimentais, sob condições de tensão senoidal/simétrica e não-senoidal/assimétrica, avaliando-se o comportamento em regime permanente e dinâmico do sistema. O algoritmo de programação linear que visa otimizar a compensação foi analisado por meio de resultados de simulaçãoAbstract: This thesis presents a flexible and robust architecture and corresponding control strategy for modern low voltage microgrids with distributed energy resources. The strategy fully exploits the potential of distributed energy resources, under grid-connected and islanded operating modes. In grid-connected mode, under global optimization mode, the control strategy pursues quasi-optimum operation of the microgrid, so as to reduce distribution loss and voltage deviations. In islanded mode, it effectively manages any available energy source to ensure a safe and smooth autonomous operation of the microgrid. Such strategy is applied to a fully-dispatchable microgrid structure, based on a master-slave control architecture, in which the distributed units are coordinated by means of the recently developed power-based control. The main advantages of the proposed architecture are the scalability (plug-and-play) and capability to run the distributed units without synchronization or knowledge of line impedances. Moreover, the proposed microgrid topology manages promptly the interaction with the mains by means of a utility interface, which is a grid-interactive inverter equipped with energy storage. This allows a number of advantages, including compensation of load unbalance, reduction of harmonic injection, fast reaction to load and line transients, and smooth transition between operating mode. On the other hand, in order to provide demand response, proportional power sharing, reactive power control, and full utilization of distributed energy resources, the microgrid employs a reliable communication link with limited bit rate that does not involve time-critical communications among distributed units. It has been shown that a communication failure does not jeopardize the system, and just impairs the global optimization mode. However, the system keeps properly operating under the local optimization mode, which is managed by a linear algorithm in order to optimize the compensation of reactive power, harmonic distortion and load unbalance by means of distributed electronic power processors, for example, active power filters and other grid-connected inverters, especially when their capability is limited. It consists in attain several power quality performance indexes, defined at the grid side and within a feasible power region in terms of the power converter capability. Based on measured load quantities and a certain objective function, the algorithm tracks the expected optimal source currents, which are thereupon used to calculate some scaling coefficients and, therefore, the optimal compensation current references. Finally, the thesis also proposes an efficient technique to control single-phase converters, arbitrarily connected to a three-phase distribution system (line-to-neutral or line-to-line), aiming for reduce unbalance load and control the power flow among different phases. It enhances the power quality at the point-of-common-coupling of the microgrid, improve voltage profile through the lines, and reduce the overall distribution loss. The master-slave microgrid architecture has been analyzed and validated by means of computer simulations and experimental results under sinusoidal/symmetrical and nonsinusoidal/asymmetrical voltage conditions, considering both the steady-state and dynamic performances. The local optimization mode, i.e., linear algorithm for optimized compensation, has been analyzed by simulation resultsDoutoradoEnergia EletricaDoutor em Engenharia Elétrica2012/24309-8, 2013/21922-3FAPES

Power Grid Network Evolutions for Local Energy Trading

The shift towards an energy Grid dominated by prosumers (consumers and producers of energy) will inevitably have repercussions on the distribution infrastructure. Today it is a hierarchical one designed to deliver energy from large scale facilities to end-users. Tomorrow it will be a capillary infrastructure at the medium and Low Voltage levels that will support local energy trading among prosumers. In our previous work, we analyzed the Dutch Power Grid and made an initial analysis of the economic impact topological properties have on decentralized energy trading. In this paper, we go one step further and investigate how different networks topologies and growth models facilitate the emergence of a decentralized market. In particular, we show how the connectivity plays an important role in improving the properties of reliability and path-cost reduction. From the economic point of view, we estimate how the topological evolutions facilitate local electricity distribution, taking into account the main cost ingredient required for increasing network connectivity, i.e., the price of cabling

Power Electronics Applications in Renewable Energy Systems

The renewable generation system is currently experiencing rapid growth in various power grids. The stability and dynamic response issues of power grids are receiving attention due to the increase in power electronics-based renewable energy. The main focus of this Special Issue is to provide solutions for power system planning and operation. Power electronics-based devices can offer new ancillary services to several industrial sectors. In order to fully include the capability of power conversion systems in the network integration of renewable generators, several studies should be carried out, including detailed studies of switching circuits, and comprehensive operating strategies for numerous devices, consisting of large-scale renewable generation clusters

Design and implementation of machine learning techniques for modeling and managing battery energy storage systems

The fast technological evolution and industrialization that have interested the humankind since the fifties has caused a progressive and exponential increase of CO2 emissions and Earth temperature. Therefore, the research community and the political authorities have recognized the need of a deep technological revolution in both the transportation and the energy distribution systems to hinder climate changes. Thus, pure and hybrid electric powertrains, smart grids, and microgrids are key technologies for achieving the expected goals. Nevertheless, the development of the above mentioned technologies require very effective and performing Battery Energy Storage Systems (BESSs), and even more effective Battery Management Systems (BMSs). Considering the above background, this Ph.D. thesis has focused on the development of an innovative and advanced BMS that involves the use of machine learning techniques for improving the BESS effectiveness and efficiency. Great attention has been paid to the State of Charge (SoC) estimation problem, aiming at investigating solutions for achieving more accurate and reliable estimations. To this aim, the main contribution has concerned the development of accurate and flexible models of electrochemical cells. Three main modeling requirements have been pursued for ensuring accurate SoC estimations: insight on the cell physics, nonlinear approximation capability, and flexible system identification procedures. Thus, the research activity has aimed at fulfilling these requirements by developing and investigating three different modeling approaches, namely black, white, and gray box techniques. Extreme Learning Machines, Radial Basis Function Neural Networks, and Wavelet Neural Networks were considered among the black box models, but none of them were able to achieve satisfactory SoC estimation performances. The white box Equivalent Circuit Models (ECMs) have achieved better results, proving the benefit that the insight on the cell physics provides to the SoC estimation task. Nevertheless, it has appeared clear that the linearity of ECMs has reduced their effectiveness in the SoC task. Thus, the gray box Neural Networks Ensemble (NNE) and the white box Equivalent Neural Networks Circuit (ENNC) models have been developed aiming at exploiting the neural networks theory in order to achieve accurate models, ensuring at the same time very flexible system identification procedures together with nonlinear approximation capabilities. The performances of NNE and ENNC have been compelling. In particular, the white box ENNC has reached the most effective performances, achieving accurate SoC estimations, together with a simple architecture and a flexible system identification procedure. The outcome of this thesis makes it possible the development of an interesting scenario in which a suitable cloud framework provides remote assistance to several BMSs in order to adapt the managing algorithms to the aging of BESSs, even considering different and distinct applications