Juan Ignacio

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As residential adoption of renewable energy sources increases, optimizing rooftop photovoltaic systems (RTPVs) with Battery Energy Storage Systems (BESSs) is key for enhancing self-sufficiency and reducing dependence on the grid. This study introduces a novel methodology for sizing Home Energy Management Systems (HEMS), with the objective of minimizing the cost of imported energy while accounting for battery degradation. The battery model integrated nonlinear degradation effects and was evaluated in a real case study, considering different temporal data resolutions and various energy management strategies. For BESS capacities ranging from 1 to 5 kWh, the economic analysis demonstrated cost-effectiveness, with a Net Present Value (NPV) ranging from 54.53 € to 181.40 € and discounted payback periods (DPBs) between 6 and 10 years. The proposed HEMS extended battery lifespan by 22.47% and improved profitability by 21.29% compared to the current HEMS when applied to a 10 kWh BESS. Sensitivity analysis indicated that using a 5 min resolution could reduce NPV by up to 184.68% and increase DPB by up to 43.12% compared to a 60 min resolution for batteries between 1 and 5 kWh. This underscores the critical impact of temporal resolution on BESS sizing and highlights the need to balance accuracy with computational efficiency.

Department of Geriatric, Hospital Universitario La Paz, 28046 Madrid, Spain

Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution's accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG.

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The investigation of new composite materials possessing low weight but not at the expense of their mechanical performance is of great interest in terms of reducing energy consumption in many industrial applications. This study is focused on the nanomechanical characterization of high-density polyethylene (HDPE)-based composite specimens modified with equal loadings of graphene nanoplatelets (GNPs) and/or multiwall carbon nanotubes (MWCNTs). Quasi-static nanoindentation analysis revealed the impact of the carbon nanofillers on the receiving of nanocomposites with higher nanohardness and reduced modulus of elasticity, reaching values of 0.146 GPa and 3.57 GPa, respectively. The role of the indentation size effect in elastic polymer matrix was assessed by applying three distinct peak forces. Nanoscratch experiments depicted the tribological behavior of the composite samples and inferred the influence of the carbon nanofillers on the values of the coefficient of friction (COF). It seems that the incorporation of 4 wt% GNPs in the polymer structure improves the scratch resistance of the material, resulting in a higher value of the exerted lateral force and therefore leading to the detection of a higher coefficient of friction at scratch of 0.401. A considerable pile-up response of the scratched polymer specimens was observed by means of

Australia Bans Influencer Candace Owens Over Holocaust Remarks Australia has barred American commentator Candace Owens from entering the