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Abstract With the application of advanced information technology for the integration of electricity and natural gas systems, formulating an excellent energy conversion and management strategy has become an effective method to achieve established goals. Differing from previous works, this paper proposes a peak load shifting model to smooth the net load curve of an integrated electricity and natural gas system by coordinating the operations of the power-to-gas unit and generators. Moreover, the study aims to achieve multi-objective optimization while considering the economy of the system. A dynamic energy conversion and management strategy is proposed, which coordinates both the economic cost target and the peak load shifting target by adjusting an economic coefficient. To illustrate the complex energy conversion process, deep reinforcement learning is used to formulate the dynamic energy conversion and management problem as a discrete Markov decision process, and a deep deterministic policy gradient is adopted to solve the decision-making problem. By using the deep reinforcement learning method, the system operator can adaptively determine the conversion ratio of wind power, power-to-gas and gas turbine operations, and generator output through an online process, where the flexibility of wind power generation, wholesale gas price, and the uncertainties of energy demand are considered. Simulation results show that the proposed algorithm can increase the profit of the system operator, reduce wind power curtailment, and smooth the net load curves effectively in real time.

Climaps.eu is an online atlas providing data, visualizations and commentaries about climate adaptation debate. It contains 33 issue-maps and 5 issue-stories. Each of the maps focuses on one issue in the adaptation debate and provides.The atlas is addressed to climate experts (negotiators, NGOs and companies concerned by global warming, journalists…) and to citizens willing to engage with the issues of climate adaptation.It employs advanced digital methods to deploy the complexity of the issues related to climate adaptation and information design to make this complexity legible.

The present paper exhibits a real time assessment of a robust Energy Management Strategy (EMS) for battery-super capacitor (SC) Hybrid Energy Storage System (HESS). The proposed algorithm, dedicated to an electric vehicular application, is based on a self-gain scheduled controller, which guarantees the H∞ performance for a class of linear parameter varying (LPV) systems. Assuming that the duty cycle of the involved DC-DC converters are considered as the variable parameters, that can be captured in real time, and forwarded to the controller to ensure both; the performance and robustness of the closed-loop system. The subsequent controller is therefore time-varying and it is automatically scheduled according to each parameter variation. This algorithm has been validated through experimental results provided by a tailor-made test bench including both the HESS and the vehicle traction emulation system. The experimental results demonstrate the overall stability of the system, where the proposed LPV supervisor successfully accomplishes a power frequency splitting in an adequate way, respecting the dynamic of the sources. The proposed solution provides significant performances for different speed levels.

Abstract In this paper, SnO2 prepared by sol-gel method was spin-coated on textured n-type crystalline silicon (c-Si) wafers to replace phosphorus doped n-type hydrogenated amorphous silicon (a-Si:H) in novel Si heterojunction (SHJ) solar cells. Good coverage of the sol-gel SnO2 layer on pyramidal textured silicon substrate was achieved. It was demonstrated that a low-work-function metal electrode is necessary for the dopant-free electron transport layer. The SnO2/Mg combination layer was determined to have a low-work-function feature and exhibit a synergistic effect in promoting the selective transport of carriers. A combination passivation layer containing intrinsic amorphous silicon (a-Si:H(i)) and SiOx which was formed from UV-O3 photo oxidation of the a-Si:H(i) surface for a short time was used to passivate the interface between the SnO2 and the c-Si. The new type a-Si:H/SiOx/SnO2/Mg contact is effective to achieve not only a low contact resistivity but also good passivation properties. Finally, a power conversion efficiency (PCE) of 18.6% and an open circuit voltage of 695 mV was achieved on a novel SHJ solar cell with an a-Si:H(i)/SiOx combination passivation layer and a SnO2/Mg combination electron selective transport layer.

Integrated energy systems become more and more important to realize the energy complementary property. Micro-integrated energy system, served as the terminal integrated energy system, will have the electricity delivered directly to the local customers by energy hubs (EHs). Here, the data and information of the EHs during the operation are confidential and should be kept by each owner. Therefore, this article designs a dual-decomposition-based distributed algorithm to address this problem, where the optimal consensus problem is used for the dual problem to update the multipliers. The primary and dual problems are alternatively solved until the Karush–Kuhn–Tucher condition is satisfied. For the proposed distributed algorithm, the feasibility can be strictly guaranteed during the iteration process. Moreover, theorems and lemmas are proved for the linear convergence rate. The numerical results verify the effectiveness of the proposed algorithm.

Abstract The objective of this article is to design and plan sustainable bio-ethanol supply chain. Modeling supply chains that achieve economic, social and environmental feasibility through production, process and energy efficiency is a challenge. Life cycle assessment that is coupled with techno-economic optimization of bio-ethanol supply chain is an alternative solution to achieve sustainability. A simulation of the biomass conversion is used to find process parameters of the conversion technology. The results show that the unified model is capable of minimizing both CO 2 emissions and energy and utility consumptions. In addition, the supply chain is capable of contributing to local economy through jobs creation. While the model is quite comprehensive, the future research recommendation on energy integration and global sustainability is proposed.

Seaports are specifically designed for trading purposes. They are equipped with facilities for handling industrial and commercial goods as well as raw materials stored in containers. These facilities are often based on diesel cranes, which are noisy and produce air pollution. A possible solution to address this problem is replacing the diesel-power cranes with the electric ones. This idea, however, demands a high power connection to the grid in the seaport. This paper presents a cost-effective and environmentally friendly solution based on an electrical flywheel system to reduce electricity consumption from the electrical power network while improving system efficiency by using already existing technologies. Besides, this study presents a new method for controlling electrical drives using flywheel energy storage systems in harbor crane applications by exploiting the energy harvested from the cranes. The system model, including the electrical grid, cranes, power electronic drives, and flywheels as energy storages, is presented and an effective control methodology is developed. Simulation results of a practical crane system are presented and discussed. Practical lab-scale setup is also built and tested. The results have shown that by using the proposed method, the energy can be effectively harvested from the crane into the flywheel energy storage system during its operation, which significantly enhances the harbor power system efficiency as well as supply quality.

Abstract A performance degradation model and a real-time remaining useful life (RUL) prediction method are proposed on the basis of temperature characteristic parameters to determine the RUL of wind turbine bearings. First, using the moving average method, the relative temperature data of wind turbine bearings are smoothed, and the temperature trend data are obtained on the basis of the uncertainty of wind speed and wind direction that causes the temperature of wind turbine bearings to vary widely. Second, given that the degradation speed of bearings changes with operational time and uncertain external factors, the performance degradation model is established with the Wiener process. The parameters of this model are obtained through the maximum likelihood estimation method. Third, according to the failure principle of the first temperature monitoring value beyond the first warning threshold, the RUL prediction model for wind turbine bearings is established on the basis of an inverse Gaussian distribution. Finally, the performance degradation process and real-time RUL prediction are demonstrated by predicting the RUL of a practical rear bearing of a wind turbine generator. The comparison of the predicted RUL and actual RUL shows that the proposed model and prediction method are correct and effective.