Walid Ali

To tackle the challenges associated with variability and uncertainty in distributed power generation, as well as the complexities of solving high-dimensional energy management mathematical models in mi-crogrid energy optimization, a microgrid energy optimization management method is proposed based on an improved soft actor-critic algorithm. In the proposed method, the improved soft actor-critic algorithm employs an entropy-based objective function to encourage target exploration without assigning signifi-cantly higher probabilities to any part of the action space, which can simplify the analysis process of distributed power generation variability and uncertainty while effectively mitigating the convergence fragility issues in solving the high-dimensional mathematical model of microgrid energy management. The effectiveness of the proposed method is validated through a case study analysis of microgrid energy op-timization management. The results revealed an increase of 51.20%, 52.38%, 13.43%, 16.50%, 58.26%, and 36.33% in the total profits of a microgrid compared with the Deep Q-network algorithm, the state-action-reward-state-action algorithm, the proximal policy optimization algorithm, the ant-colony based algorithm, a microgrid energy optimization management strategy based on the genetic algorithm and the fuzzy inference system, and the theoretical retailer stragety, respectively. Additionally, com-pared with other methods and strategies, the proposed method can learn more optimal microgrid energy management behaviors and anticipate fluctuations in electricity prices and demand.

A major challenge facing humans in the 21st century is how to strike a balance between the mitigation of environmental degradation and the achievement of sustainable economic growth. On this note, this investigation applies an autoregressive distributed lag (ARDL) methodology to a panel data of 28 European Union (EU-28) countries over the period 2000-2020. The study confirms the existence of positive and significant long-run nexus among environmental sustainability, renewable energy consumption and economic growth in the EU-28 countries. In addition, empirical results indicate that real gross fixed capital formation, carbon emissions and other environmental factors are principal determinants of long-run growth in the EU. Using Dumitrescu and Hurlin (2012) Granger non-causality in heterogeneous panel, results show long-run bidirectional causal relationships among renewable energy consumption, economic growth and other growth determinants. Based on these results, infer that the exploitation of renewable energy sources in the EU-28 countries is a reliable pathway toward environmental pollution mitigation. This indicates that achieving the sustainable development goals (SDGs) by the year 2030 through renewable energy consumption and carbon emission mitigation is very much achievable in the EU-28 countries, and should also be adopted by all countries as an effective global policy.