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

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

FedZero: Leveraging Renewable Excess Energy in Federated Learning

Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing. Yet, FL inevitably introduces inefficiencies compared to centralized model training, which will further increase the already high energy usage and associated carbon emissions of machine learning in the future. Although the scheduling of workloads based on the availability of low-carbon energy has received considerable attention in recent years, it has not yet been investigated in the context of FL. However, FL is a highly promising use case for carbon-aware computing, as training jobs constitute of energy-intensive batch processes scheduled in geo-distributed environments. We propose FedZero, a FL system that operates exclusively on renewable excess energy and spare capacity of compute infrastructure to effectively reduce the training's operational carbon emissions to zero. Based on energy and load forecasts, FedZero leverages the spatio-temporal availability of excess energy by cherry-picking clients for fast convergence and fair participation. Our evaluation, based on real solar and load traces, shows that FedZero converges considerably faster under the mentioned constraints than state-of-the-art approaches, is highly scalable, and is robust against forecasting errors

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

Management and modelling of battery storage systems in microGrids and virtual power plants

In the novel smart grid configuration of power networks, Energy Storage Systems (ESSs) are emerging as one of the most effective and practical solutions to improve the stability, reliability and security of electricity power grids, especially in presence of high penetration of intermittent Renewable Energy Sources (RESs). This PhD dissertation proposes a number of approaches in order to deal with some typical issues of future active power systems, including optimal ESS sizing and modelling problems, power ows management strategies and minimisation of investment and operating costs. In particular, in the first part of the Thesis several algorithms and methodologies for the management of microgrids and Virtual Power Plants, integrating RES generators and battery ESSs, are proposed and analysed for four cases of study, aimed at highlighting the potentialities of integrating ESSs in different smart grid architectures. The management strategies here presented are specifically based on rule-based and optimal management approaches. The promising results obtained in the energy management of power systems have highlighted the importance of reliable component models in the implementation of the control strategies. In fact, the performance of the energy management approach is only as accurate as the data provided by models, batteries being the most challenging element in the presented cases of study. Therefore, in the second part of this Thesis, the issues in modelling battery technologies are addressed, particularly referring to Lithium-Iron Phosphate (LFP) and Sodium-Nickel Chloride (SNB) systems. In the first case, a simplified and unified model of lithium batteries is proposed for the accurate prediction of charging processes evolution in EV applications, based on the experimental tests on a 2.3 Ah LFP battery. Finally, a dynamic electrical modelling is presented for a high temperature Sodium-Nickel Chloride battery. The proposed modelling is developed from an extensive experimental testing and characterisation of a commercial 23.5 kWh SNB, and is validated using a measured current-voltage profile, triggering the whole battery operative range

You are what you measure! But are we measuring it right? An empiric analysis of energy access metrics based on a multi-tier approach in Bangladesh

Measuring energy access through binary indicators is insufficient, and often, even misleading. In this work, the SE4ALL global tracking framework, and the recently introduced ESMAP multi-tier approach, is critically discussed analyzing questionnaire based primary data from rural Bangladesh. The performance of different energy interventions is evaluated using the new tier framework. The challenges in its application lie in reliable data collection, adequate gradation of indicators, and an effective algorithm for the tier assignment based on the specified set of attributes. The study showcases very high sensitivities to parameter changes, different algorithms, and data requirements. The results reveal a clear trade-off between capturing the multi-dimensionality of energy access and the simplicity of an easy to use global framework. Suggestions to improve the measuring approach are made and conclusions are drawn for possible implications of the tier framework for different energy service offers in the market. Strengths and weaknesses of the present measurement scheme are discussed and country specific results interpreted through targeted gap analysis for future policy advice

Thermoeconomic and environmental optimization of polygeneration systems for small-scale residential buildingsintegrating thermal and electric energy storage, renewable energy and legal restrictions.

El sector residencial, responsable del 27% del consumo energético mundial y 17% de emisiones de gases de efecto invernadero aproximadamente, desempeña un papel clave para combatir el cambio climático. Por esto, el uso de sistemas de poligeneración resulta una alternativa apropiada para cubrir las demandas energéticas de los edificios, ya que permiten un uso eficiente de los recursos naturales con un bajo impacto ambiental. En este sentido, esta tesis ha desarrollado un modelo de programación lineal entera mixta (MILP) para investigar estos sistemas de forma sistemática, integrando tecnologías renovables, como la solar y eólica, con almacenamiento de energía térmica y eléctrica, considerando equipos comerciales, teniendo en cuenta aspectos económicos y ambientales en el diseño. La investigación comienza por la forma de abordar el proceso de optimización, partiendo por la elección del método para seleccionar días representativos. Comparando diferentes métodos, se demuestra que su idoneidad depende en gran medida de la variabilidad de las series temporales involucradas en el sistema analizado. Además, se ha desarrollado un nuevo método que mejora los resultados del proceso de optimización. Por otro lado, se ha estudiado la viabilidad del uso de edificios residenciales como microrred. El estudio muestra que resultan rentables con respecto a los sistemas energéticos convencionales actuales, pero es necesario la aplicación de incentivos o permitir la venta de electricidad a un precio razonable para que sean competitivos. Adicionalmente, se han estudiado e identificado sinergias entre los componentes del sistema energético gracias al desarrollo de un modelo termoeconómico, que muestran la importancia de abordar el diseño de los sistemas energéticos considerando conjuntamente tecnologías térmicas y eléctricas, destacando la bomba de calor y los acumuladores de energía como tecnologías claves para lograr soluciones más económicas y sostenibles. Finalmente, se han aplicado las últimas regulaciones españolas de autoconsumo para evaluar su impacto económico y ambiental en el diseño de sistemas energéticos. Además, a través de la aplicación de la optimización multiobjetivo, se analizó si la reciente regulación de autoconsumo se ajusta a las metas europeas e internacionales para combatir el cambio climático. Asimismo, se estudia cómo podría abordarse la regulación para promover el desarrollo de sistemas energéticos sostenibles para el sector residencial. Los resultados sugieren actuar sobre la regulación de autoconsumo para reducir el impacto ambiental de forma efectiva. En general, esta tesis proporciona metodologías e ideas útiles para el diseño de sistemas energéticos sostenibles capaces de cubrir las demandas de energía de los edificios residenciales.The residential sector, responsible of about 27% of the global energy consumption and 17% of the greenhouse gas emissions, plays a key role in the action to combat climate change. In this sense, polygeneration systems could be considered a suitable alternative to attend the energy demands of residential buildings since they enable an efficient use of natural resources with a low environmental impact. This thesis developed a Mixed Integer Linear Programming (MILP) model to research these kind of systems in a systematic way to integrate renewable energy such as solar and wind energy with thermal and electric energy storage, considering commercial equipment for small-medium scale residential buildings, taking into account both economic and environmental aspects for the optimal design of such systems. The research starts from the suitable way to address the optimization process focused on the selection of the method to select representative days. Through the comparison of different methods, it was demonstrated that its right selection strongly depends on the variability of the time series involved in the analysed system. Besides, a new method was developed in order to improve the results of the optimization process. The developed MILP model was applied to study the feasibility of residential buildings as a microgrid. This innovative approach was found profitable with respect to the current conventional energy systems but it is necessary the application of feed-in tariff schemes or allowing the sale of electricity at reasonable price in order to make them competitive. Further, a thermoeconomic analysis was carried out to evaluate synergies between the components of the energy system. It was shown the importance of considering both thermal and electrical parts in the design of energy systems, highlighting the role of heat pumps and energy storage as key technologies, to achieve more cost-effective and sustainable solutions. Finally, the recent Spanish self-consumption regulations were applied to evaluate its impact on the design of energy systems. Moreover, through the application of multiobjective optimization and the analysis of different trade-off solutions was evaluated if this regulation aligned with European and international goals to combat climate change, and how it could be addressed in order to promote the design of affordable sustainable energy supply systems for the residential buildings. The obtained results suggest to act on the self-consumption regulation in order to achieve more significant reduction of greenhouse gas emissions. Overall, this thesis provided methodologies and useful insights for the design of sustainable energy systems for residential buildings.<br /