Uncertainties In Energy Systems Research Articles

Entropy as a factor of influence on energy security management of enterprises

Energy security management of the enterprise in conditions of entropy is an important aspect that includes adaptation to changes in the external environment and internal processes. The object of research is entropy, as a measure of uncertainty and chaos in energy systems, which affects the energy security management of enterprises. The problem of a comprehensive approach to the study and implementation of methods for calculating entropy indicators, which would take into account entropy in the energy security management, is solved. The conducted analysis shows how the level of entropy in the supply of energy resources, in particular coal, electricity and alternative sources, affects the stability, sustainability and adaptability of management strategies aimed at ensuring energy security management. The essence of the obtained results is that the study shows the importance of energy security management of enterprises in the conditions of entropy, which is a measure of uncertainty in energy systems. Entropy acts as a key factor influencing the energy security management, as it reflects the level of chaos and uncertainty in the supply of energy resources. It is shown that the level of entropy directly affects the stability and adaptability of management strategies, which allows enterprises to better respond to external challenges and internal risks. The use of mathematical models, in particular Shannon's formulas, makes it possible to quantitatively assess the level of entropy and identify potential risks arising in energy systems. Awareness of the impact of entropy on management decisions helps enterprises to optimize processes, predict threats and reduce negative consequences. The research results reflect the complex interplay between entropy, management strategies and external challenges, emphasizing the importance of an adaptive approach in energy security management. The research focuses on practical aspects. From a practical point of view, awareness of the impact of entropy on management decisions allows enterprises to optimize management processes, predict potential threats, and reduce the negative consequences of external and internal risks. The results of this research can become the basis for the formation of new management strategies capable of effectively responding to the modern challenges of the energy sector.

Stochastic Model Predictive Control for Robust Grid Frequency Regulation

This paper delves into the application of Stochastic Model Predictive Controls (SMPC) for power grids driven by inverter-interfaced generators, focusing on enhancing grid stability amidst decreasing inertia. By employing SMPC, uncertainties in energy systems are anticipated and plant-model mismatch is mitigated. Improvement in grid robustness concerning frequency limits is demonstrated via a Monte Carlo approach. The integration of data-driven model augmentation and stochastic constraint tightening significantly enhance the precision and robustness of frequency control. This study highlights the potential of SMPC in navigating uncertainties in energy systems and offering a robust framework for maintaining grid stability.

Stochastic framework for planning studies of energy systems: a case of EHs

The substantial presence of different uncertainties in energy systems highlights the need for probabilistic analysis of operational and planning studies. Motivated by this fact, a new stochastic planning framework for energy hubs (EHs) is presented in this study based on the probability transformation concept. In the proposed framework, a measure of relative-likelihood impact is developed to estimate the importance of uncertainty sources in the studies, which, in turn, can remarkably reduce the overall complexity of stochastic problems. Step-by-step algorithm for implementation of the proposed framework on planning studies of an EH is also addressed in this study. Three different case studies are introduced, and the results provide some insightful information regarding the impact of different uncertainty sources in the system.

Load-side Frequency Regulation with Limited Control Coverage

Increasing renewable energy increases uncertainty in energy systems. As a consequence, generator-side control for frequency regulation, impacted by the slow reaction of generators to meet urgent needs, may no longer suffice. With increasing integration of smart appliances which are able to sense, communicate and control, load-side control can help alleviate the aforementioned problem as it reacts fast and helps to localize disturbances. However, almost all existing methods for optimal load-side control require full information control coverage in the system. Framing the problem as an optimization problem and applying saddle-point dynamics, we obtain a control law that rebalances power and asymptotically stabilizes frequency after a disturbance. We generalize previous work to design a controller which only requires partial control coverage over all nodes, yet still achieves secondary frequency control. We verify these results via simulation.

Robust Optimization for Strategic Energy Planning

Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices. When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favour of fossil alternatives. This work aims at overcoming this issue by assessing the impact of uncertainty on energy planning decisions. A classification of uncertainty in energy systems decision-making is performed. Robust optimization is then applied to a Mixed-Integer Linear Programming problem, representing the typical trade-offs in energy planning. It is shown that in the uncertain domain investing in more efficient and cleaner technologies can be economically optimal.

Development of an integrated optimization method for analyzing effect of energy conversion efficiency under uncertainty – A case study of Bayingolin Mongol Autonomous Prefecture, China

In this study, a superiority–inferiority full-infinite mixed-integer programming (SFMP) method is developed for analyzing the effect of energy conversion efficiency under uncertainty. SFMP can effectively tackle uncertainties expressed as fuzzy sets, crisp intervals and functional intervals, it also can directly reflect relationships among multiple fuzzy sets through varying superiority and inferiority degrees with a high computational efficiency. Then the developed SFMP is applied to a real case of planning energy system for Bayingolin Mongol Autonomous Prefecture, where multiple scenarios related to different energy-conversion efficiency are concerned. Results for energy processing, energy conversion, capacity expansion, pollutant emission and system cost have been generated. It is proved that SFMP is an effective approach to deal with the uncertainties in energy systems with interactive and uncertain characteristics. A variety of uncertainties existed in energy conversion processes and impact factors could affect the modeling result. Results show that improvement of energy-conversion efficiency can effectively facilitate reducing energy resources consumption, optimizing energy generation pattern, decreasing capacity expansion, as well as mitigating pollutant emissions. Results also reveal that, for the study area, electric power has a highest energy saving potential among heating, oil processing, coal washing and refining. Results can help decision makers to generate desired alternatives that can facilitate policy enactment of conversion efficiency improvement and adjustment of regional energy structure under uncertainty.

Modeling uncertainty of induced technological change

Modeling uncertainty of induced technological change