Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs

Pool trading model within a local energy community considering flexible loads, photovoltaic generation and energy storage systems

This paper presents a pool trading model within a local energy community considering home energy management systems (HEMSs) and other consumers. A transparent mechanism for market clearing is proposed to incentivise active prosumers to trade their surplus energy within a rule-based pool market in the local energy community. A price-based demand response program (PBDRP) is considered to increase the consumers’ willingness to modify their consumption. The mathematical optimization problem is a standard mixed-integer linear programming (MILP) problem to allow for rapid assessment of the trading market for real energy communities which have a considerable number of consumers. This allows for novel energy trading strategies amongst different clients in the model and for the integration of a pool energy trading model at the level of the local energy community. The objective function of the energy community is to minimize the overall bills of all participants while fulfilling their demands. Two different scenarios have been evaluated, independent and integrated operation modes, to show the impacts of coordination amongst different end-users. Results show that through cooperation, end-users in the local energy community market can reduce the total electricity bill. This is shown in a 16.63% cost reduction in the independent operation and a 21.38% reduction in the integrated case. Revenues for active consumers under coordination increased compared to independent operation of the HEMS.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

renewable sources integration through the optimization of the load for residential applications

Abstract This work presents the implementation of two different control strategies for the control of Microgrids a Model Predictive Control (MPC) technique coupled with a Mixed-Integer Linear Program (MILP) structure and a Rule Based Control (RBC) strategy both applied to a residential MicroGrid. The validation of the models has been performed with an experimental setup laid out in the laboratory of University of Rome - Tor Vergata. Results obtained show that MicroGrids connected to the main network have enough potential to support grid balancing actions, thus allowing for a greater penetration of renewable sources into the mix, and giving economic benefits for both end users and providers. In particular, using a MPC strategy major benefits can be obtained in terms of reduction of the unbalanced energy exchange with the main grid and a more efficient use of the micro-grid components

Energy resource management in smart buildings considering photovoltaic uncertainty

O aumento do consumo energético em edifícios residenciais tem levado a um maior foco nos métodos de eficiência energética. Deste modo, surge um sistema de gestão de energia residencial que poderá permitir controlar os recursos energéticos em pequena escala dos edifícios, levando a uma diminuição significativa dos custos energéticos através de um escalonamento eficiente. No entanto, a natureza intermitente das fontes de energia renováveis resulta num problema complexo. Para resolver este desafio, esta tese propõe um escalonamento energético baseado na otimização robusta, considerando a incerteza relacionada com a produção fotovoltaica. A otimização robusta é um método emergente e eficaz para lidar com a incerteza e apresenta soluções ótimas considerando o pior cenário da incerteza, ou seja, encontra a melhor solução entre todos os piores cenários possíveis. Um problema de Programação Linear Binária é inicialmente formulado para minimizar os custos do escalonamento energético. De seguida, o objetivo desta tese é transformar o modelo determinístico num problema robusto equivalente para proporcionar-lhe imunidade contra a incerteza associada à produção fotovoltaica. O modelo determinístico é, assim, transformado num modelo do pior cenário possível. Para validar a eficiência e a eficácia do modelo, a metodologia proposta foi implementada em dois cenários sendo cada um deles constituído por três casos de estudo de escalonamento de energia, para um horizonte de escalonamento a curto prazo. Os resultados da simulação demonstram que a abordagem robusta consegue, efetivamente, minimizar os custos totais de eletricidade do edifício, mitigando, simultaneamente, os obstáculos referentes à incerteza relacionada com a produção fotovoltaica. É também demonstrado que a estratégia desenvolvida permite o ajustamento do escalonamento dos recursos energéticos do edifício de acordo com o nível de robustez selecionado.The increase of energy demand in residential buildings has led to a higher focus on energy efficiency methods. This way, the home energy management system arises to control small-scale energy resources on buildings allowing a significant electricity bill decrease throughout efficient scheduling. However, the intermittent and uncertain nature of renewable energy sources results in a complex problem. To solve this challenge, this thesis proposes robust optimization-based scheduling considering the uncertainty in solar generation. Robust Optimization is a very recent and effective technique to deal with uncertainty and provides optimal solutions for the worst-case realization of the uncertain parameter, i.e., it finds the best solution among all the worst scenarios. A Mixed Binary Linear Programming problem is initially formulated to minimize the costs of the energy resource scheduling. Then, this thesis's purpose is to transform the deterministic model into a trackable robust counterpart problem to provide immunity against the photovoltaic output uncertainty. The deterministic model is transformed into the worst-case model. To validate the model’s efficiency and effectiveness, the proposed methodology was implemented in two scenarios with three different energy scheduling case studies for a short-term scheduling horizon. The simulation results demonstrate that the robust approach can effectively minimize the electricity costs of the building while mitigating the drawbacks associated with solar uncertainty. It also proves that the proposed strategy adjusts the energy scheduling according to the selected robustness level

Grid-connected Microgrids to Support Renewable Energy Sources Penetration

Abstract Distributed generation systems and microgrids are instrumental for a greater penetration of renewables to achieve a substantial reduction on carbon emissions. However, microgrids performances and reliability strongly depend on the continuous interaction between power generation, storage and load requirements, highlighting the importance in developing a proper energy management strategy and the relative control system. In this work a Model predictive Control (MPC) strategy, based on a Mixed Linear Integer Programming framework, has been applied to a residential microgrid case. Theoretical results obtained confirm that grid connected microgrids have potential capabilities in grid balancing allowing for a larger penetration of fluctuating renewable energy sources and thus producing economic benefits for both end-user and grid operators. A microgrid test bench to reproduce previous microgrid model is also presented in the paper. The experimental setup has been used to validate results obtained from simulation. Results obtained confirm the potential of this solution and its real applicability

Programação Robusta de Energia para Edifícios Inteligentes considerando a Incerteza em Veículos Eléctricos

Nos últimos anos, o consumo de energia tem aumentado juntamente com o crescimento económico e populacional, onde os edifícios representam um dos principais consumidores. Contudo, surgem preocupações a nível ambiental as quais inspiram governos a concentrarse na concepção de edifícios inteligentes com sistemas de gestão de energia que controlam as fontes de energia renováveis. No entanto, um fator importante a considerar ao lidar com os recursos energéticos é a natureza incerta do seu comportamento. De forma a dar resposta a este desafio, esta tese consiste em propor uma programação ótima dos recursos energéticos baseado na otimização robusta, tendo em conta as incertezas associadas aos veículos elétricos. A otimização robusta é uma abordagem inovadora e eficaz para resolver problemas de otimização que envolvem incerteza, uma vez que encontra a melhor solução entre os piores cenários possíveis. Inicialmente é formulada uma técnica de Redes Neuronais Artificiais, de modo a lidar com as incertezas. Posteriormente, um problema de Programação Linear Binária é estipulado para reduzir os custos energéticos do edíficio sem considerar incertezas. Numa fase final, o modelo determinístico é transformado num problema robusto, assegurando imunidade contra a incerteza associada aos veículos elétricos. De modo a simular o modelo de Otimização Robusta foram implementados três cenários diferentes de programação energética com um horizonte de tempo curto. Os resultados apresentaram uma redução de 14.86% no caso do estado da carga inicial, de 6.75% para a hora de chegada e de 14.18% para a hora de partida, revelando que o modelo implementado permite minimizar os custos totais de eletricidade de um edifício, bem como reduzir os problemas associados à incerteza dos veículos elétricos. Além disso, é demonstrado o ajustamento da técnica de otimização robusta de acordo com vários níveis de robustez.In recent years, electricity consumption has increased along with economic and population growth, with buildings representing one of the main consumers. However, environmental concerns are emerging and inspiring governments to focus on designing intelligent buildings with energy management systems that control renewable energy sources. However, an important factor to consider when dealing with energy resources is the uncertain nature of their behavior. To address this challenge, this thesis proposes optimal scheduling of energy resources based on robust optimization, taking into account the uncertainties associated with electric vehicles. Robust optimization is an innovative and effective approach for solving optimization problems involving uncertainty since it finds the best solution among the worst-case scenarios. Initially, an Artificial Neural Networks technique is formulated to deal with uncertainties. Afterward, a Binary Linear Programming problem is stipulated to reduce the energy costs of the building without considering uncertainties. In the final step, the deterministic model is transformed into a robust problem, ensuring immunity against the uncertainty related to electric vehicles. To simulate the Robust Optimization model, three different energy scheduling scenarios with a short time horizon were implemented. The results showed a reduction of 14.86% for the initial State Of Charge (SoC), 6.75% for the arrival time, and 14.18% for the departure time, revealing that the implemented model allows for minimizing the total electricity costs of a building, as well as reducing the problems associated with the uncertainty of electric vehicles. In addition, the adjustment of the robust optimization technique according to various levels of robustness is demonstrated

Multi-objective preemptive optimization of residential load scheduling problem under price and CO2 signals

Abstract: This paper addresses the residential load scheduling problem with the objective of investigating the influence of price and CO2 signals in (i) the electricity bill, (ii) the consumer inconvenience, (iii) the electric peak load, and (iv) the CO2 emissions. These objectives were considered widely in the literature; however, they were not considered simultaneously in one model before. Furthermore, CO2 emissions targets constraint was not considered in the previous literature. This paper contributes by twofold. First, the CO2 signal is drawn up based on the proposed generation-mix plan in South Africa and an hourly CO2 emissions limit is guaranteed. Second, a multi-objective mixed integer programming model is proposed, and a preemptive multi-objective optimization approach is applied. The model is tested with and without considering the hourly CO2 emissions limit. Furthermore, the model is solved at four scenarios to explore the effect of the price and CO2 signals and the order of optimization. Results showed that the price and CO2 signals and the order of optimization have remarkable effect on the appliance schedule and on the four objectives

Household CO₂-efficient energy management

Abstract Residential and commercial buildings are responsible for one third of the total (CO2) emissions in the European Union, which are the main cause of global warming. Although the thermal load has long been considered the primary reason of domestic energy consumptions, the increasing demand for electricity has a non-negligible environmental impact, given that about 40% of electricity is generated by burning fossil fuels. Moreover, the amount of CO2 emitted to produce one kWh can greatly vary in time, depending on the sources used to generate it. For instance, the German electricity emissions intensity factor varied in 2017 between 113 and 533 gCO2eq/kWh. This paper proposes a novel CO2-efficient energy management approach to schedule household appliances while minimizing carbon dioxide emissions, given the possibility to change energy carriers (i.e., natural gas and electricity) and to shift loads in time. Several common loads are considered, and their operation is scheduled according to the emission factor of the German power grid. The results show that switching energy carriers can successfully enable up to 40% emissions reductions while indicating that shifting loads in time has little impact