In response to the growing demand for sustainable energy and the environmental impacts of fossil fuels, renewable sources like biomass have become crucial, especially in regions rich in agricultural and animal waste. This study focuses on a real-life project in Aras de los Olmos, Spain, where solar, wind, and biogas from biomass serve as primary energy sources, supplemented by a hydro-based storage system to stabilize supply. Central to the research is optimizing biomass inflow to the biogas reactor—the primary controllable variable—to effectively manage the supply chain, maximize energy output, and minimize logistical costs. The study addresses practical challenges by utilizing real data on demand, truck capacities, and costs and employing robust optimization tools like Gurobi. It demonstrates how optimized biomass flow can secure energy needs during high demand or when other renewables are unavailable. Integrating technical and economic aspects, it offers a comprehensive and practical model for sustainable and economically viable energy production in rural communities. It provides a foundational framework for future renewable energy and optimized energy storage system studies.

As global energy demand increases, ensuring a reliable electricity supply in rural or semi-remote areas remains a significant challenge. Hybrid energy systems, which integrate renewables, generators, storage, and grid connections, offer a promising solution for addressing energy reliability issues. In this context, the rural community of Aras de los Olmos, Spain, serves as the focal point because of its frequent power outages despite being connected to the main grid. This study investigates innovative solutions tailored to the community’s unique needs. It highlights critical challenges in achieving reliable energy access and bridges the gap between existing limitations and sustainable, future-oriented energy systems. This is achieved by analyzing the current energy setup and evaluating potential alternatives. Two scenarios were evaluated: one optimizing the existing configuration for economic efficiency while retaining the grid as the primary energy source, and another introducing a biomass generator to enhance reliability by partially replacing the grid. Detailed technical, financial, and environmental assessments were performed using HOMER. These assessments identified an optimal configuration. This optimal configuration improves reliability, enhances stability, reduces disruptions, and meets growing energy demands cost-effectively. As will be indicated, the first scenario can reduce total costs to approximately USD 90,000 compared to the existing setup, whereas the second scenario can lower grid dependence by approximately 70%. In addition, introducing renewable energy sources, such as solar and biomass, significantly reduces greenhouse gas emissions and reliance on fossil fuels. Additionally, these solutions create local job opportunities, promote community engagement, support energy independence, and align with broader sustainability goals.

High-resolution time-series data are crucial for effective microgrid planning; however, such datasets are often incomplete or unavailable. This study presents a lightweight and transparent framework for generating realistic synthetic time series of energy variables. The method combines (i) probabilistic modeling to fit statistical distributions to segmented hourly data, and (ii) a trend-informed correction mechanism that preserves hour-to-hour continuity based on observed transition patterns. The proposed framework is validated using high-resolution datasets from a rural site in Spain, first against historical records from 2019 to 2023 used for model construction, and then against an independent dataset from 2023 to 2024 to assess its generalization to unseen data. Results show that finer temporal segmentation improves local statistical accuracy, while the trend-informed correction significantly reduces abrupt transitions, leading to smoother, more realistic temporal behavior. Across the conducted evaluations, the refined framework achieves improvements of up to approximately 30% in selected metrics and variables. In addition, the proposed approach is benchmarked against representative classical and neural time-series models, demonstrating comparable or improved point-wise accuracy under a consistent evaluation setting, with benchmark-case percentage prediction error reductions of approximately up to 35% across the evaluated variables. Consistent improvements across validation horizons confirm the robustness of the method. Rather than relying on heavily tuned or fully data-driven deep learning approaches, the proposed framework combines classical probabilistic modeling with a trend-based correction layer, achieving realistic temporal sequences at minimal computational cost. The resulting synthetic data preserves temporal realism while remaining transparent and data-efficient, facilitating microgrid sizing, storage optimization, and resilience analysis.