• Energy Research

Digitalization has brought a significant improvement in process optimization and decision-making processes, in particular in pursuing the goal of sustainability. This study examines how digitalization has affected process optimization and decision-making towards sustainability, focusing on Pakistan’s manufacturing sector. This study also examines the moderating role of environmental regulations between digitalization and sustainable practices. This study is based on quantitative methodology. Purposive sampling was used to gather primary data from 554 managers and engineers working in manufacturing industries in Pakistan through a closed-ended questionnaire. Smart PLS was used for data analysis. The findings show digitalization’s positive and significant influence on process optimization and decision-making. The results also show that environmental regulations have a significant moderating effect on the digitalization of processes and decision-making towards sustainability practices. The findings provide a guideline for industries, decision-makers, and researchers for developing strategies that effectively use digitalization for sustainability and assist in achieving the Sustainable Development Goals (SGD-9, SGD-11, SGD-12, and SGD-13).

AbstractThis paper presents a novel approach to time series forecasting, an area of significant importance across diverse fields such as finance, meteorology, and industrial production. Time series data, characterized by its complexity involving trends, cyclicality, and random fluctuations, necessitates sophisticated methods for accurate forecasting. Traditional forecasting methods, while valuable, often struggle with the non-linear and non-stationary nature of time series data. To address this challenge, we propose an innovative model that combines signal decomposition and deep learning techniques. Our model employs Generalized Autoregressive Conditional Heteroskedasticity (GARCH) for learning the volatility in time series changes, followed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for data decomposition, significantly simplifying data complexity. We then apply Graph Convolutional Networks (GCN) to effectively learn the features of the decomposed data. The integration of these advanced techniques enables our model to fully capture and analyze the intricate features of time series data at various interval lengths. We have evaluated our model on multiple typical time-series datasets, demonstrating its enhanced predictive accuracy and stability compared to traditional methods. This research not only contributes to the field of time series forecasting but also opens avenues for the application of hybrid models in big data analysis, particularly in understanding and predicting the evolution of complex systems.

This paper concerns the development and analysis of multigeneration systems based on hybrid sources such as biomass and wind. Industry requires different types of sources to provide several outputs, so the goal of this research was to fulfill the industrial requirement with optimization. The multigeneration cycle supplies enough power to satiate energy demands, i.e., power, cooling, hydrogen, air conditioning, freshwater, hot water, and heating. For this, the multigeneration cycle was modeled in the Engineering Equation Solver (EES) and Simulink to obtain optimized results for the industry. Energy and exergy for the multigeneration cycle were determined to assess the performance of the cycle and to investigate the optimized results for the overall system. This study shows that for configuration selection and design, different thermodynamic, economic, and environmental aspects should be considered. Based on the results, the selection of the best location for this multigeneration system was made. Power output from the wind turbine was around 7 MW and from biogas 0.6 MW. The overall exergy efficiency of the multigeneration system was found to be 0.1401.

Abstract This paper develops a machine learning aggregated integer linear programming approach for the full observability of the automated smart grids by positioning of micro-synchrophasor units, taking into account the reconfigurable structure of the distribution systems. The proposed stochastic approach presents a strategy occurring in several stages to micro-synchrophasor unit positioning based on the load level and demand in the system and based on the pre-determined sectionalizing and tie switches. Such a technique can also deploy the zero-injection limitations of the model and reduce the search space of the problem. Moreover, a novel method based on whale optimization method (WOM) is introduced to simultaneously enhance the reliability indices in order to specify the optimum topology for each phase and reduce the costs of power losses and customer interruptions. Although the problem of micro-synchrophasor placement is formulated in an integer linear programming framework, the restructuring technique is resolved on the basis of the WOM heuristic approach. Considering the uncertainty due to the metering devices or forecast errors, a stochastic framework based on point estimation is deployed to handle the uncertainty effects. The simulation and numerical results on a real system verify that the proposed method assures visibility of the distribution network pre and post reconfiguration in the time horizon of the planning. Furthermore, the results show that the system observability can be guaranteed at different load levels even though the system experiences different reconfiguration and topologies.

The study investigates the relationship between green production, green technology, waste reduction, energy use, and sustainability. A Partial Least Squares Structural Equation Modeling (PLS-SEM) approach was used for analysis. The data was collected from a sample of companies in the textile industry. The results suggest that green production and technology positively and significantly affect waste reduction and energy use, which mediates the positive relationship between these two factors and sustainability. This study concludes that green production and technology are critical drivers of sustainability and emphasizes the need to prioritize waste reduction and energy use in sustainable manufacturing practices. The study has practical and managerial implications in all production or manufacturing industries and provides a guideline for managers and policymakers to ensure sustainability.

Abstract This article introduces an effective stochastic operation framework for optimal energy management of the shipboard power systems including large, nonlinear and dynamic loads. The proposed framework divides the ship power system into several agents, which coordinate with each other based on their demands/supplies until. The alternating direction method of multipliers (ADMM) is deployed as the multi-agent framework to solve the reformulated distributed energy management problem in the ship. Two types of turbo-generators are considered in the proposed system model, including single-shaft and twin-shaft models, to increase the part-load efficiency in certain times when facing variable speed operation. The proposed distributed framework is equipped with a recursive mechanism, which helps the ship system for running optimal load scheduling when facing insufficient power generation. In order to model the uncertainty effects associated with the forecast error in the interval-ahead load demand, a stochastic framework based on unscented transform is devised which can work in the nonlinear and correlated environments of shipboard power systems. Due to the nonlinear cost function in each agent, a powerful optimization algorithm based on modified θ-firefly algorithm (Mθ-FOA) is proposed. This is a phasor algorithm, which helps for escaping from premature convergence and getting trapped in local optima. The appropriate performance of the proposed stochastic model is examined on the real dataset of a ship power system. The simulation results show the high robustness, guarantied consensus, economic operation and feasible solution when power generation shortage based on load shedding in the system.