RESUMEN
The integration of traditional state estimation techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) with modern artificial neural networks (ANNs) presents a promising avenue for advancing state estimation in sustainable energy systems. This study explores the potential of hybridizing EKF-UKF with ANNs to optimize renewable energy integration and mitigate environmental impact. Through comprehensive experimentation and analysis, significant improvements in state estimation accuracy and sustainability metrics are revealed. The results indicate a substantial 8.02 % reduction in estimation error compared to standalone EKF and UKF methods, highlighting the enhanced predictive capabilities of the hybrid approach. Moreover, the integration of ANNs facilitated a 12.52 % increase in renewable energy utilization efficiency, leading to a notable 5.14 % decrease in carbon emissions. These compelling outcomes underscore the critical role of hybrid approaches in maximizing the efficiency of sustainable energy technologies while simultaneously reducing environmental footprint. By harnessing the synergies between traditional filtering techniques and machine learning algorithms, hybrid EKF-UKF with ANNs emerges as a key enabler in accelerating the transition towards a more sustainable and resilient energy landscape.
RESUMEN
Many challenges have emerged due to the intense integration of renewables in the distribution system and the associated uncertainties in power generation. Consequently, local management strategies are developed at the distribution level, leading to the emergence of concepts such as microgrids. Microgrids include a variety of heating, cooling, and electrical resources and loads, and the operators' aim is to minimize operation and outage costs. Since significant distribution system outages are typically caused by events such as earthquakes, floods, and hurricanes, microgrid operators are compelled to improve resilience to ensure uninterrupted service during such conditions. A mixed-integer linear programming model is designed in this paper to optimize the energy management and structural configuration of microgrids. This optimization aims to enhance resilience cost, minimizing operation and capital costs as well as power loss and pollution. To achieve these goals, several tools are implemented including reconfiguration, storages, combined cooling, heat and power units, wind turbines, photovoltaic panels, as well as capacitors. Four case studies are defined to prove the developed model efficiency. The first case study focuses on energy management in the microgrid for operation cost minimization. The second case study emphasizes the improvement of resilience alongside energy management, aiming at minimizing costs and enhance resilience. In the third case, the microgrid's reconfiguration capability is also added to the second case. Therefore, this case aims to optimize both energy and structural management within the microgrid to simultaneously enhance resilience and minimize operational costs. Finally, in the fourth case, the problem is studied in a multi-objective approach. By comparing the results, the resilience impact on the operation of microgrids is elucidated. By considering the resilience concept in microgrid operation and based on the results of case 2, it is found that the operating costs are increased by an average of 10.38 %. However, because of reducing resilience costs by an average of 13.91 %, the total cost is reduced by an average of 5.93 % in case 2 compared to case 1. Furthermore, when comparing cases 2 and 3, the reconfiguration effect can be determined. It can be observed that the operating costs are decreased by an average of 4.5 %. Moreover, the resilience cost is decreased by an average of 1.61 %, resulting in an overall reduction of the total objective function by an average of 2.43 % in case 3 compared to case 2.
RESUMEN
Development of the multigeneration plants based on the simultaneous production of water and energy can solve many of the current problems of these two major fields. In addition, the integration of fossil power plants with waste heat recovery processes in order to prevent the release of pollutants in the environment can simultaneously cover the environmental and thermodynamic improvements. Besides, the addition of a carbon dioxide (CO2) capturing cycles with such plants is a key issue towards a sustainable environment. Accordingly, a novel waste heat recovery-based multigeneration plant integrated with a carbon dioxide separation/liquefaction cycle is proposed and investigated under multi-variable assessments (energy/exergy, financial, and environmental). The offered multigeneration system is able to generate various beneficial outputs (electricity, liquefied CO2 (L-CO2), natural gas (NG), and freshwater). In the offered system, the liquified natural gas (LNG) cold energy is used to carry out condensation processes, which is a relatively new idea. Based on the results, the outputs rates of net power, NG, L-CO2, and water were determined to be approximately 42.72 MW and 18.01E+03, 612 and 3.56E+03 kmol/h, respectively. Moreover, the multigeneration plant was efficient about 32.08% and 87.72%, respectively, in terms of energy and exergy. Economic estimates indicated that the unit product costs of electricity and liquefied carbon dioxide production, respectively, were around 0.0466 USD per kWh and 0.0728 USD per kg-CO2. Finally, the total released CO2 was about 0.034 kg per kWh. According to a comprehensive comparison, the offered multigeneration plant can provide superior environmental, thermodynamic, and economic performances compared to similar plants. Moreover, there was no need to purchase electricity from the grid.