Energies

In view of the increasing environmental challenges and the growing demand for sustainable energy solutions, the optimization of microgrid systems with regard to economic efficiency and environmental compatibility is becoming ever more important. This paper presents the Microgrid Performance and Investment Rating (MPIR) index, a novel assessment framework developed to link economic and environmental objectives within microgrid configurations. The MPIR index evaluates microgrid configurations based on five critical dimensions: financial viability, sustainability, regional renewable integration readiness, energy demand, and community engagement, facilitating comprehensive and balanced decision making. The current cases focus on the area of Greece; however, the model can have a wider application. Developed using a two-target optimization model, this index integrates various energy sources—including photovoltaics, micro-wind turbines, and different types of batteries—with advanced energy management strategies to assess and improve microgrid performance. This paper presents case studies in which the MPIR index is applied to different microgrid scenarios. It demonstrates its effectiveness in identifying optimal configurations that reduce the carbon footprint while maximizing economic returns. The MPIR index provides a quantifiable, scalable tool for stakeholders, not only advancing the field of microgrid optimization, but also aligning with global sustainability goals and promoting the transition to a more resilient and sustainable energy future.

Reducing energy consumption in the construction sector is urgently needed. In Chile, where income distribution is unequal and the cost of energy is high, energy demand is seriously affected, especially in vulnerable households. Hence, it is essential to establish public policies with more realistic energy-saving goals to address this situation. However, reliably predicting the energy performance of buildings remains a challenge. For this reason, this study aims to identify and evaluate the impact of the variables associated with energy performance in vulnerable households in Central-Southern Chile and propose values that would reduce the gap. A sensitivity analysis was conducted to achieve this, adjusting the energy performance parameters in a base model with data analyzed using local standards. In addition, field information was collected in 93 households to obtain the actual energy consumption. The main results show that the variables that most impacted performance were infiltration, COP, heating setpoints, and schedules, which generated a 60% difference between the theoretical and actual consumption.

To monitor temperature as a function of varying inductance and resistance, we propose a data-driven digital twin approach for the rapid and efficient real-time estimation of the rotor temperature in an induction motor. By integrating differential equations with online signal processing, the proposed data-driven digital twin approach is structured into three key stages: (1) transforming the nonlinear differential equations into discrete algebraic equations by substituting the differential operator with the difference quotient based on the sampled voltage and current; (2) deriving approximate analytical solutions for rotor resistance and stator inductance, which can be utilized to estimate the rotor temperature; and (3) developing a general procedure for obtaining approximate analytical solutions to nonlinear differential equations. The feasibility and validity of the proposed method were demonstrated by comparing the test results with a 1.5 kW AC motor. The experimental results indicate that our method achieves a minimum estimation error that falls within the standards set by IEC 60034-2-1. This work provides a valuable reference for the overheating protection of induction motors where direct temperature measurement is challenging.

To alleviate the power flow congestion in active distribution networks (ADNs), this paper proposes a two-stage load transfer optimization model based on Neo4j-Dueling DQN. First, the Neo4j graph model was established as the training environment for Dueling DQN. Meanwhile, the power supply paths from the congestion point to the power source point were obtained using the Cypher language built into Neo4j, forming a load transfer space that served as the action space. Secondly, based on various constraints in the load transfer process, a reward and penalty function was formulated to establish the Dueling DQN training model. Finally, according to the ε−greedy action selection strategy, actions were selected from the action space and interacted with the Neo4j environment, resulting in the optimal load transfer operation sequence. In this paper, Python was used as the programming language, TensorFlow open-source software library was used to form a deep reinforcement network, and Py2neo toolkit was used to complete the linkage between the python platform and Neo4j. We conducted experiments on a real 79-node system, using three power flow congestion scenarios for validation. Under the three power flow congestion scenarios, the time required to obtain the results was 2.87 s, 4.37 s and 3.45 s, respectively. For scenario 1 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by about 56.0%, 76.0% and 55.7%, respectively. For scenario 2 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 41.7%, 72.9% and 56.7%, respectively. For scenario 3 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 13.6%, 47.1% and 37.7%, respectively. The experimental results show that the trained model can quickly and accurately derive the optimal load transfer operation sequence under different power flow congestion conditions, thereby validating the effectiveness of the proposed model.

The increasing level of anthropogenic CO2 in the atmosphere has made it imperative to investigate an efficient method for carbon sequestration. Geological carbon sequestration presents a viable path to mitigate greenhouse gas emissions by sequestering the captured CO2 deep underground in rock formations to store it permanently. Geochemistry, as the cornerstone of geological CO2 sequestration (GCS), plays an indispensable role. Therefore, it is not just timely but also urgent to undertake a comprehensive review of studies conducted in this area, articulate gaps and findings, and give directions for future research areas. This paper reviews geochemistry in terms of the sequestration of CO2 in geological formations, addressing mechanisms of trapping, challenges, and ways of mitigating challenges in trapping mechanisms; mineralization and methods of accelerating mineralization; and the interaction between rock, brine, and CO2 for the long-term containment and storage of CO2. Mixing CO2 with brine before or during injection, using microbes, selecting sedimentary reservoirs with reactive minerals, co-injection of carbonate anhydrase, and enhancing the surface area of reactive minerals are some of the mechanisms used to enhance mineral trapping in GCS applications. This review also addresses the potential challenges and opportunities associated with geological CO2 storage. Challenges include caprock integrity, understanding the lasting effects of storing CO2 on geological formations, developing reliable models for monitoring CO2–brine–rock interactions, CO2 impurities, and addressing public concerns about safety and environmental impacts. Conversely, opportunities in the sequestration of CO2 lie in the vast potential for storing CO2 in geological formations like depleted oil and gas reservoirs, saline aquifers, coal seams, and enhanced oil recovery (EOR) sites. Opportunities include improved geochemical trapping of CO2, optimized storage capacity, improved sealing integrity, managed wellbore leakage risk, and use of sealant materials to reduce leakage risk. Furthermore, the potential impact of advancements in geochemical research, understanding geochemical reactions, addressing the challenges, and leveraging the opportunities in GCS are crucial for achieving sustainable carbon mitigation and combating global warming effectively.