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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.

Telecommunication is a prerequisite for the Smart Grid. Several technologies are available on the market, but there is not a general agreement about which of them, or combination of them, that can enable most of the services for the distribution grid automation. The paper describes the approach of 7th Framework Program European Project INTEGRIS which relies on the hypothesis that there is not a single technology with an optimal trade-off between cost of investment and performances for all use cases and topologies of a distribution grid. It rather suggests to mesh several technologies (with focus on the Broadband Power Line, the Wi-Fi and the Fiber Optics) to cover all the segment of the distribution grid. This concept has been experimentally validated in two field demonstrators in Italy and in Spain. This paper presents the results of tests on the latency and the throughput.

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.

The proposed special issue (SI) has invited submissions related to renewable energy, energy storage, power converters and electric drive systems for electrified transportation and smart grid applications [...]

Abstract Given the need to reduce the CO2 emissions coming from the manufacturing sector, it is important, for planning purposes, to know which countries and which manufacturing sub-sectors have the greatest potential for reducing energy use. Using data from the International Atomic Energy Agency and the United Nations Industrial Development Organization, the authors estimate trends in global decoupling of energy use and manufacturing value added, compare energy-use intensity in six country groups and estimate the potential for reducing energy use and CO2 emissions under two scenarios and compare selected sub-sector energy intensity and estimate the potential for reducing energy use CO2 emissions. The comparison of energy intensities across country groups and among countries suggests that there still remains significant potential to reduce energy use and associated CO2 emissions. The analysis of four sub-sectors in developing and transition economies also shows similar but varied potential for reducing energy use and associated CO2 emissions.

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.