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The path congestion and communication risks of electric power communication network have emerged as the carried businesses are becoming more and more heavy. In this paper, a multi-objective optimization method for power communication network routing considering the importance of the carried businesses is proposed to reduce load imbalance and transmission risk. Firstly, information entropy is introduced as the objective of communication network load balancing optimization. And a risk assessment model for power communication network nodes and links is established. Then fuzzy membership functions are adopted to singularize the two objectives to jointly optimize path congestion and communication risks. The device nodes are dynamically divided according to the carried businesses to pursue the global optimization of the power communication network in which the Yen's algorithm is adopted. Finally, this paper uses the IEEE 30-bus communication network to carry out case simulations and analyzes the proposed method to verify the effectiveness and feasibility.

Demand side management is a useful and necessary tool in smart grid energy management system to reduce total power demand during peak demand periods and hence, enhancing grid sustainability and reducing overall cost. This paper discusses a new load shifting approach for demand side management in smart grid energy management. This approach optimizes the consumption curves of household, commercial and industrial consumers. The proposed algorithm in this approach minimizes the cost incurred by users while taking into account users' individual preferences for the loads by setting priorities and preferred time intervals for load scheduling.

With the fast growth of the global economy, energy supply and demand have a strong impact on social, economic, and environmental aspects. As a consequence, this has pushed the decision-makers to formulate objectives, guiding economic policies toward sustainable goals. The process is known as Sustainable Development Goals (SDGs) that have been proposed by the United Nations. This being said, the energy sector is a vital domain with a vast potential for improvments in terms of technologies and ligistalations. Solar energy is among the most efficient solutions proposed to reduce the economic and environmental footprints of energy. In this frame, the current paper aims to localize solar energy within SDGs and analyze the contribution of the solar energy towards the achievement of the SDGs. Moreover, the current work highlights the contributions of Mohammed bin Rashid Al Maktoum (MBR) Solar Park in the United Arab Emirates to achieving the SDGs. Indeed, the MBR Solar Park concept offers valuable insights of environmental impacts by deploying clean and affordable energy sources in place of conventional fossil fuel power plants that are still heavily used in the region. The MBR Solar Park operation has already mitigated 6.5 million tonnes of carbon dioxide equivalent and this number will likely rise when all phases are installed and operational. Moreover, it has been shown that MBR Solar Park achieve several SDGs such SDG 8: decent work and economic growth, SDG 9: industry, innovation and infrastructure, SDG 11: sustainable cities and communities, and SDG 15: life on land.

Increasing the use of electric vehicles (EVs) is regarded as a step in the right direction to reduce air pollution and carbon emissions. However, a dramatic increase of EV and charging stations has raised voltage quality and harmonic distortion issues that affect the performance of integrated renewable power sources (wind and solar) and smart-grid electrical transmission networks. This paper models an integrated electric vehicle charging and battery storage system operating in the presence of unpredictable wind and solar power sources. The aim is to enable the design of an electrical control system that develops the correct duty cycle to stabilize and regulate the voltage at the dc/dc power conversion station. Simulations are performed to evaluate energy management by the proposed control system. The proposed system effectively manages the electric power on the grid by drawing power from the batteries at peak times and then charging them in off-peak times, reducing the load on the converter and enabling the reduction of charging time for electric vehicles. A constant voltage is achieved on the grid irrespective of fluctuations in renewable energy generation and in the load.

Closed-loop supply chain networks are gaining research popularity due to environmental, economic and social concerns. Such networks are primarily designed to overcome carbon footprints and to retrieve end of life products from customers. This study considers a multi echelon closed-loop supply chain in the presence of machine disruption. A multi-objective model is presented to optimize the total cost, the total time and emissions in a closed-loop supply chain network. The aim is to analyze the trade-off between the objectives of cost, time, and emissions and how these decisions are impacted by the selection of different available machines. A number of solution approaches are tested on a case study from the tire industry. The results suggest the improved performance of the hybrid heuristic and the importance of controlling disruption in a closed-loop supply chain network. Furthermore, there is a trade-off between the different objective functions which can help the decision maker to choose a particular solution according to the preference of an organization. Finally, conclusion and future research avenues are provided.

Abstract Mitigating the effects of environmental deterioration requires a focus on not just CO2 emissions from energy consumption, but also environmental pollution from industry sectors. To reach this goal, recent studies have extended ecological footprint (EF) analysis to identify the ecological drivers of various key industry sectors. The role of the phosphorus (P) industry on the EF within the environmental Kuznets curve (EKC) framework for China is the emphasis of this study. Autoregressive distributive lag (ARDL) as well as the impulse response function and robustness analysis were used to consider a time from 1985 to 2018. The study verifies the EKC hypothesis for China in both the long and the short run, and indispensable determinants are proposed to be included to assure the model’s fitness and robustness when conducting EF analysis of industry sectors. Energy consumption–based carbon emissions have been verified as the dominant contributor to EF, but P use and urbanization have a significant lagged positive influence on EF in the short run. P exports, in particular, have been highlighted as a critical driver of the EF of China’s P industry. The conducted frequency domain causality test reinforced the above findings and demonstrated bidirectional causality at different frequencies. This work suggests that formulating plausible P export policies to alleviate the conflict between the output of China’s P industry and the environmental sustainability of this industry are necessary. In this context, “multidisciplinary, multidimensional, and practical solutions” are most desirable for sustainable P management.

It is imperative to increase the connectable capacity (i.e., hosting capacity) of distributed generation in order to decarbonise electricity distribution networks. Hybrid generation that exploits complementarity in resource characteristics among different renewable types potentially provides value for minimising technical constraints and increasing the effective use of the network. Tidal, wave and wind energy are prominent offshore renewable energy sources. It is of importance to explore their potential complementarity for increasing network integration. In this work, the novel introduction of these distinct offshore renewable resources into hosting capacity evaluation enables the quantification of the benefits of various resource combinations. A scenario reduction technique is adapted to effectively consider variation of these renewables in an AC optimal power flow-based nonlinear optimisation model. Moreover, the beneficial impact of active network management (ANM) on enhancing the renewable complementarity is also investigated. The combination of complementary hybrid generation and ANM, specifically where the maxima of the generation profiles rarely co-occur with each other and with the demand minimum, is found to make the best use of the network components.

Over the past decade, there has been a constant spotlight on introducing sustainability in the supply chain (SC). The materialistic human greed for production and consumption has led to a radically increased level of greenhouse gases. SC has become its principal contributor. We are addressing this socio-economic environmental challenge by developing a multi-stakeholder framework and focusing on a knowledge-based net zero supply chain, as there are no concrete existing studies that have investigated current state-of-the-art operations in this relevant field. Therefore, this research has been conducted to investigate the drivers, barriers and practices through which net zero economy (NZE) can be attained in a knowledge-based SC. In this regard, the paper conducts an exploratory systematic review of selected articles from peer-reviewed journals. The findings indicate that primary stakeholders (i.e. organisations and suppliers) require to take an active role in bringing about sustainable changes in practice. However, external perspectives (i.e. government, society, consumers and community) have also been identified as sources that create challenges as well as have the potential to aid sustainable industrial practices. Additionally, progress can be enhanced through proper policies, regulations and a knowledge-based conceptual framework to pave the way for a sustainable environment. Proper practices for NZE also provide scope for economic growth through cost-effective production. This paper will be beneficial for practitioners as well as policy makers on a global scale who aim to attain NZE for sustainability.