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.

Population 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 different 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.

Renewable 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.

This paper proposes a new method for the simultaneous determination of the optimal control parameters of proportional resonant controllers and the optimal design of the output filter of a grid-tied three-phase inverter. The proposed method, based on the grey wolf optimization (GWO) algorithm, addresses both optimization problems as a single process to achieve a better system frequency response. It optimizes the unknown parameters by using a fitness function to find the best trade-off between the following fundamental terms: the harmonic attenuation rate; the power loss, through the damping resistor; and the current tracking error in the stationary frame, ensuring the system and grid stability. To validate the proposed optimization methodology, two case studies are considered with different output filter topologies with passive damping methods. The results obtained from the proposed optimization procedure were analyzed and discussed according to the fitness function terms.

Recent regulatory advancements by the European Union have positioned Renewable Energy Communities (RECs) at the forefront of energy policy discussions, reigniting significant interest and debate within the scientific community. However, despite all the developments, RECs still face several challenges that need to be addressed and researched to ensure their development and economic sustainability. One key challenge is the creation of effective and fair energy-sharing mechanisms. Thus, this study proposes a Home Energy Management System (HEMS) and a Two-Stage Collaborative Energy Management Strategy (TCEMS) to manage a REC based on both technical and economic aspects, promoting an efficient operation that meets both individual and collective requirements. This is a cooperative and collaborative strategy in which renewable energy production resources and storage systems are shared in an integrated and equitable manner, ensuring a fair and balanced distribution of renewable energy within the REC. In the strategy’s first stage, a dynamic Key of Repartition (KoR) is employed to adaptively allocate renewable energy production among REC participants. In the second stage, Mixed Binary Integer Programming (MBIP) is used to efficiently manage energy surpluses and deficits, ensuring their optimal allocation across participants storage systems. To evaluate the proposed strategy’s performance, a one-year computational simulation was conducted, followed by an experimental validation in a pilot REC over a 24-hour period. The results confirmed the effectiveness and robustness of the strategy, indicating reduced dependence on the utility power grid and optimized utilization of local energy resources.

This paper shows the potential of artificial intelligence (AI) in Li-ion battery charging methods by introducing a new charging algorithm based on artificial neural networks (ANNs). The proposed charging algorithm is able to find an optimized charging current profile, through ANNs, considering the real-time conditions of the Li-ion batteries. To test and validate the proposed approach, a low-cost battery management system (BMS) was developed, supporting up to 168 cells in series and n cells in parallel. When compared with the multistage charging algorithm, the proposed charging algorithm revealed a shorter charging time (7.85%) and a smaller temperature increase (32.95%). Thus, the results show that the proposed algorithm based on AI is able to effectively charge and balance batteries and can be regarded as a subject of interest for future research.