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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.

This paper studies the newly emerging wireless powered communication network (WPCN) in which one hybrid access point (H-AP) with constant power supply coordinates the wireless energy/information transmissions to/from distributed users that do not have energy sources. A "harvest-then-transmit" protocol is proposed where all users first harvest the wireless energy broadcast by the H-AP in the downlink (DL) and then send their independent information to the H-AP in the uplink (UL) by time-division-multiple-access (TDMA). First, we study the sum-throughput maximization of all users by jointly optimizing the time allocation for the DL wireless power transfer versus the users' UL information transmissions given a total time constraint based on the users' DL and UL channels as well as their average harvested energy values. By applying convex optimization techniques, we obtain the closed-form expressions for the optimal time allocations to maximize the sum-throughput. Our solution reveals "doubly near-far" phenomenon due to both the DL and UL distance-dependent signal attenuation, where a far user from the H-AP, which receives less wireless energy than a nearer user in the DL, has to transmit with more power in the UL for reliable information transmission. Consequently, the maximum sum-throughput is achieved by allocating substantially more time to the near users than the far users, thus resulting in unfair rate allocation among different users. To overcome this problem, we furthermore propose a new performance metric so-called common-throughput with the additional constraint that all users should be allocated with an equal rate regardless of their distances to the H-AP. We present an efficient algorithm to solve the common-throughput maximization problem. Simulation results demonstrate the effectiveness of the common-throughput approach for solving the new doubly near-far problem in WPCNs. 26 pages, 12 figures

Global energy consumption has risen enormously over the past century due to population growth and increasing energy use per person. Industrial production consumes a significant portion of global energy resources. Thus, industrial sector’s investment in energy efficiency is critical to a sustainable future. For most global enterprises the consumption of energy and natural resources represents a major overhead and developing sustainable energy policies can represent a significant competitive advantage due to the growing price of energy and volatility of supply. This symbiotic relationship can lead to the mutual benefits of increasing industrial efficiency whilst allowing the transition to a sustainable renewables-based energy future and needs to be significantly harnessed. This paper describes a decision support framework to help industrial organisations make positive investment decisions on energy performance improvement projects.

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.

Renewable energy technological innovation (RETI) is an important way to reduce carbon emissions and achieve sustainable development. Exploring whether RETI is beneficial to the improvement of carbon emission efficiency and how the market environment affects the role of RETI on carbon emission efficiency is critical to the design of effective policies. Therefore, based on the data from 25 provinces in China from 2002 to 2015, the Tobit fixed-effect model and the panel threshold model are used to investigate the impact and the mechanism of RETI on total factor carbon performance index (TCPI) from a market perspective. The results show that: (1) RETI can effectively improve TCPI, but this effect is affected by market factors; (2) With the reduction of market segmentation or the increase of market potential, the improvement effect of RETI on TCPI is significantly enhanced; (3) The panel threshold model further verifies that the impact of RETI on TCPI has a significant single threshold effect in terms of market segmentation and market potential; (4) There is an inverted "U-shaped" relationship between market segmentation and TCPI, and the increase of market potential is conducive to the improvement of TCPI. This paper provides corresponding policy implications for China to achieve the dual goals of economic transformation and carbon emission reduction.

Abstract This paper studies the impact of oil price changes on economic growth in Saudi Arabia to measure its dependency on the crude oil sector. To this end, an On/Off threshold regression is specified to allow the oil/GDP relationship to be asymmetric, nonlinear, and time-varying with regard to the business cycle phases. Further, we empirically test the diversification hypothesis put forward by the National Transformation Program (Saudi Vision 2030) to check whether the equity-energy investment initiative could boost economic growth in Saudi Arabia. First, our findings confirm the contribution of the oil sector to economic growth in the country, but also show that the oil/Saudi economy relationship exhibits nonlinearity and threshold effects, as the impact of oil price varies per regime depending on the state of the market. Second, in line with the Vision 2030 expectations, we are in favor of transforming the economy and opening its equity market. We also quantify a positive and significant impact of equity investment on the Saudi Arabian economy. Further, this diversification route will stimulate a beneficial oil effect on the real economy.

Abstract With the rapid rise of installed nuclear power in China, meeting the increasing demands on natural uranium and rationally treating the vast spent fuel are essential issues for the sustainable development of Chinese nuclear power industry. This paper discusses four most potential nuclear fuel cycle modes in China and analyzes the natural uranium requirements under these different fuel cycle modes first based on three development patterns (low-, medium-, and high-speed) of installed nuclear power capacity. Then, an optimization model including natural uranium requirements, spent fuel final disposal amounts and total cost of electricity generation is constructed and optimization problem under two scenarios of reprocessing capacity are solved and results discussed. The annual and cumulative natural uranium requirements under these two scenarios are also calculated. Finally some conclusions are put forward based on the analyses.