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Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions ? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand. • A mathematical peer-to-peer trading model is presented to improve resilience. • A blockchain-based smart grid is presented for consumer energy consumption. • Five machine learning algorithms are presented in energy demand management. • Longitudinal energy consumption data of Texas is used in the model. • Theoretical support is provided using the theory of planned behavior. [ABSTRACT FROM AUTHOR]

This paper investigated the effects of phase change materials (PCMs) on temperature field and rutting performance of asphalt concrete (AC) pavement with a focus on PCM types, contents, and locations. To quantify their effects on rut depth accumulation, a pavement finite element (FE) model was constructed with measured material properties and designed structural configuration. The implemented traffic and environmental conditions were obtained from a FE model updating scheme and a heat transfer model with the consideration of traffic speed and the phase change process. Results showed that using polyethylene glycol of 4000 molar mass encapsulated by SiO 2 as the PCM to replace 10% of the aggregate in the asphalt top sublayer yielded the best rutting performance; a 14% reduction in the rut depth that was accumulated within one month after pavement construction was predicted for a typical summer case in Texas. This study also revealed that incorporating PCMs into AC does not always lead to pavement rutting performance improvement. This is because (1) the lower thermal conductivity of the PCMs can offset the benefits achieved from the increased heat capacity during phase change, and (2) PCMs will not function if they are added in layers where pavement temperature is not high enough for PCMs to undergo the phase change. Therefore, developing PCMs with higher thermal conductivity together with a judicious selection of PCM types and locations will maximize the effectiveness of the pavement temperature and rutting control. [ABSTRACT FROM AUTHOR]