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- Intelligent micro-cogeneration systems for residential grids: a sustainable solution for efficient energy managementPublication . Cardoso, Daniel; Nunes, Daniel Figueira; Faria, João; Fael, Paulo; Gaspar, Pedro DinisThis paper presents an optimization approach for Micro-cogeneration systems with internal combustion engines integrated into residential grids, addressing power demand failures caused by intermittent renewable energy sources. The proposed method leverages machine learning techniques, control strategies, and grid data to improve system flexibility and efficiency in meeting electricity and domestic hot water demands. Historical residential grid data were analysed to develop a machine learning-based demand prediction model for electricity and hot water. Thermal energy storage was integrated into the Micro-cogeneration system to enhance flexibility. An optimization model was created, considering efficiency, emissions, and cost while adapting to real-time demand changes. A control strategy was designed for the flexible operation of the Micro-cogeneration system, addressing excess thermal energy storage and resource allocation. The proposed solution’s effectiveness was validated through simulations, with results demonstrating the Micro-cogeneration system’s ability to efficiently address high electricity and hot water demand periods while mitigating power demand failures from renewable energy sources. The research presents a novel approach with the potential to significantly improve grid resilience, energy efficiency, and renewable energy integration in residential grids, contributing to more sustainable and reliable energy systems.
- Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage SystemPublication . Faria, João; Pombo, José; Calado, M. do Rosário; Mariano, S.Standalone microgrids with photovoltaic (PV) solutions could be a promising solution for powering up off-grid communities. However, this type of application requires the use of energy storage systems (ESS) to manage the intermittency of PV production. The most commonly used ESSs are lithium-ion batteries (Li-ion), but this technology has a low lifespan, mostly caused by the imposed stress. To reduce the stress on Li-ion batteries and extend their lifespan, hybrid energy storage systems (HESS) began to emerge. Although the utilization of HESSs has demonstrated great potential to make up for the limitations of Li-ion batteries, a proper power management strategy is key to achieving the HESS objectives and ensuring a harmonized system operation. This paper proposes a novel power management strategy based on an artificial neural network for a standalone PV system with Li-ion batteries and super-capacitors (SC) HESS. A typical standalone PV system is used to demonstrate and validate the performance of the proposed power management strategy. To demonstrate its effectiveness, computational simulations with short and long duration were performed. The results show a minimization in Li-ion battery dynamic stress and peak current, leading to an increased lifespan of Li-ion batteries. Moreover, the proposed power management strategy increases the level of SC utilization in comparison with other well-established strategies in the literature.
- Low-Cost IoT Remote Sensor Mesh for Large-Scale Orchard MonitorizationPublication . Varandas, Leonor Cristina Pinheiro dos Santos; Faria, João; Gaspar, Pedro Dinis; Aguiar, MartimPopulation growth and climate change lead agricultural cultures to face environmental degradation and rising of resistant diseases and pests. These conditions result in reduced product quality and increasing risk of harmful toxicity to human health. Thus, the prediction of the occurrence of diseases and pests and the consequent avoidance of the erroneous use of phytosanitary products will contribute to improving food quality and safety and environmental land protection. This study presents the design and construction of a low-cost IoT sensor mesh that enables the remote measurement of parameters of large-scale orchards. The developed remote monitoring system transmits all monitored data to a central node via LoRaWAN technology. To make the system nodes fully autonomous, the individual nodes were designed to be solar-powered and to require low energy consumption. To improve the user experience, a web interface and a mobile application were developed, which allow the monitored information to be viewed in real-time. Several experimental tests were performed in an olive orchard under di erent environmental conditions. The results indicate an adequate precision and reliability of the system and show that the system is fully adequate to be placed in remote orchards located at a considerable distance from networks, being able to provide real-time parameters monitoring of both tree and the surrounding environment.
- Power Management Strategy for Standalone PV Applications with Hybrid Energy Storage SystemPublication . Faria, João; Pombo, José Álvaro Nunes; Mariano, S.; Calado, M. do RosárioHybrid energy storage systems that combine the high density power of the supercapacitors with the high energy density of the Li-ion batteries have been substantially researched, with great interest. This manuscript proposes a new power management strategy implemented using the Rule based controller which considers the state of charge of the Li-ion batteries (SOC). The Li-ion batteries are used to provide a steady and constant power, function of the state of charge (SOC), enabling a higher efficiency and extending batteries lifespan. A typical standalone PV application is used to demonstrate and validate the performance of the proposed power management strategy.
- Optimal Sizing of Renewable Energy Communities: A Multiple Swarms Multi-Objective Particle Swarm Optimization ApproachPublication . Faria, João; Marques, Carlos; Pombo, José; Mariano, Sílvio; Calado, M. do RosárioRenewable energy communities have gained popularity as a means of reducing carbon emissions and enhancing energy independence. However, determining the optimal sizing for each production and storage unit within these communities poses challenges due to conflicting objectives, such as minimizing costs while maximizing energy production. To address this issue, this paper employs a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with multiple swarms. This approach aims to foster a broader diversity of solutions while concurrently ensuring a good plurality of nondominant solutions that define a Pareto frontier. To evaluate the effectiveness and reliability of this approach, four case studies with different energy management strategies focused on real-world operations were evaluated, aiming to replicate the practical challenges encountered in actual renewable energy communities. The results demonstrate the effectiveness of the proposed approach in determining the optimal size of production and storage units within renewable energy communities, while simultaneously addressing multiple conflicting objectives, including economic viability and flexibility, specifically Levelized Cost of Energy (LCOE), Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR). The findings also provide valuable insights that clarify which energy management strategies are most suitable for this type of community.
- Estratégias de Operação para Sistemas Fotovoltaicos com Armazenamento Híbrido de Energia ElétricaPublication . Faria, João Pedro Domingos; Calado, Maria do Rosário AlvesNa sociedade atual a energia elétrica é considerada um bem essencial para a vida de cada indivíduo. De modo a acompanhar esta crescente tendência na utilização de energia elétrica, surge cada vez mais uma implícita responsabilidade da sociedade na procura de fontes de energia mais limpas. Uma das mais atrativas soluções neste momento passa pela produção fotovoltaica. A utilização desse tipo de energia, obtida por meio da transformação direta de recursos naturais, é atualmente estudada com grande interesse pela comunidade científica, devido à sua complexidade, tanto pelas diferentes fontes de produção, quanto pela sua variabilidade e imprevisibilidade. No entanto, essa falta de previsibilidade poderia ser compensada pela complementaridade entre recursos ou pela introdução de sistemas de armazenamento de energia elétrica. Os sistemas de armazenamento de energia elétrica são reconhecidos como uma das abordagens mais promissoras. No entanto, estes sistemas sofrem de alguns problemas operacionais, como por exemplo a degradação do desempenho quando sujeitos a altas correntes de carga / descarga e uma consequente redução da sua vida útil. Para mitigar estas desvantagens, começaram a surgir os sistemas de armazenamento híbridos de energia. Estes sistemas combinam benefícios de duas ou mais tecnologias diferentes. A ligação de super-condensadores e baterias de Li-ion, que combina a alta densidade de potência de super-condensadores com a alta densidade energética das baterias de Li-ion, é a topologia mais usual neste tipo de sistema. Esta dissertação tem como objetivo o dimensionamento, construção e controlo de um sistema isolado da rede elétrica, com extração de energia fotovoltaica e armazenamento híbrido de energia elétrica.