A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle

This paper addresses the problem of optimizing charging/discharging schedules of electric vehicles (EVs) when participate in demand response (DR). As there exist uncertainties in EVs' remaining energy, arrival and departure time, and future electricity prices, it is quite difficult to make charging decisions to minimize charging cost while guarantee that the EV's battery state-of-the-charge (SOC) is within certain range. To handle with this dilemma, this paper formulates the EV charging scheduling problem as a constrained Markov decision process (CMDP). By synergistically combining the augmented Lagrangian method and soft actor critic algorithm, a novel safe off-policy reinforcement learning (RL) approach is proposed in this paper to solve the CMDP. The actor network is updated in a policy gradient manner with the Lagrangian value function. A double-critics network is adopted to synchronously estimate the action-value function to avoid overestimation bias. The proposed algorithm does not require strong convexity guarantee of examined problems and is sample efficient. Comprehensive numerical experiments with real-world electricity price demonstrate that our proposed algorithm can achieve high solution optimality and constraints compliance

Remote Sensing Evaluation of CLM4 GPP for the Period 2000-2009*

Remote sensing can provide long-term and large-scale products helpful for ecosystem model evaluation. The authors compare monthly gross primary production (GPP) simulated by the Community Land Model, version 4 (CLM4) at a half-degree resolution with satellite estimates of GPP from the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (MOD17) for the 10-yr period January 2000–December 2009. The assessment is presented in terms of long-term mean carbon assimilation, seasonal mean distributions, amplitude and phase of the annual cycle, and intraannual and interannual GPP variability and their responses to climate variables. For the long-term annual and seasonal means, major GPP patterns are clearly demonstrated by both products. Compared to the MODIS product, CLM4 overestimates the magnitude of GPP for tropical evergreen forests. CLM4 has a longer carbon uptake period than MODIS for most plant functional types (PFTs) with an earlier onset of GPP in spring and a later decline of GPP in autumn. Empirical orthogonal function analysis of the monthly GPP changes indicates that, on the intraannual scale, both CLM4 and MODIS display similar spatial representations and temporal patterns for most terrestrial ecosystems except in northeast Russia and in the very dry region of central Australia. For 2000–09, CLM4 simulated increases in annual averaged GPP over both hemispheres; however, estimates from MODIS suggest a reduction in the Southern Hemisphere (−0.2173 PgC yr−1), balancing the significant increase over the Northern Hemisphere (0.2157 PgC yr−1). The evaluations highlight strengths and weaknesses of the CLM4 primary production and illuminate potential improvements and developments

Processing conditions and mechanisms for the plasma defect-engineering of bulk oxygen-deficient zirconia

In recent years, the utilisation of oxygen-deficient zirconia (ZrO2-α), commonly referred to as black zirconia, has garnered considerable attention due to its potential applications for solid oxide fuel cells (SOFCs), gas sensors, biomedical implant materials, and photocatalysis. However, current methods employed to manufacture ZrO2-α exhibit noticeable limitations regarding their scalability, environmental sustainability, and cost-effectiveness. Our recent work has successfully demonstrated the feasibility for bulk conversion of conventional white zirconia into oxygen-deficient black zirconia through direct current (DC) plasma treatment (i.e. plasma blackening). This study elucidates the conditions for plasma blackening and provides a unique mechanism for the bulk transformation of zirconia. A systematic investigation of different plasma technologies (DC, active-screen plasma), treatment configurations (contact conditions, cathode material, and cathode potential), and treatment parameters (voltage, temperature, duration) uncover the crucial variables that influence the feasibility and rate of the reduction process. The reduction of zirconia is shown to initiate from localised contacting points at the cathode-facing surface and grow, with a hemispherical shape, towards the anode-facing surface. A series of development stages are proposed for the process, namely: bulk oxygen vacancy conductance, surface activation, oxygen vacancy generation and a moving cathode front. The findings of this study provide insights into the underlying mechanisms involved in the bulk-reduction of zirconia and help to pave the way towards future scalable and cost-effective generation of oxygen-deficient zirconia

Note on the Persistence of a Nonautonomous Lotka-Volterra Competitive System with Infinite Delay and Feedback Controls

We study a nonautonomous Lotka-Volterra competitive system with infinite delay and feedback controls. We establish a series of criteria under which a part of n-species of the systems is driven to extinction while the remaining part of the species is persistent. Particularly, as a special case, a series of new sufficient conditions on the persistence for all species of system are obtained. Several examples together with their numerical simulations show the feasibility of our main results

Feedback between carbon and nitrogen cycles during the Ediacaran Shuram excursion

This research is supported by the National Natural Science Foundation of China (41872032, 41830215, 41930320) and the Chinese ‘111’ project (B20011).The middle Ediacaran Period records one of the deepest negative carbonate carbon isotope (δ13Ccarb) excursions in Earth history (termed the Shuram excursion). This excursion is argued by many to represent a large perturbation of the global carbon cycle. If true, this event may also have induced significant changes in the nitrogen cycle, because carbon and nitrogen are intimately coupled in the global ocean. However, the response of the nitrogen cycle to the Shuram excursion remains ambiguous. Here, we reported high resolution bulk nitrogen isotope (δ15N) and organic carbon isotope (δ13Corg) data from the upper Doushantuo Formation in two well-preserved sections (Jiulongwan and Xiangerwan) in South China. The Shuram-equivalent excursion is well developed in both localities, and our results show a synchronous decrease in δ15N across the event. This observation is further supported by bootstrapping simulations taking into account all published δ15N data from the Doushantuo Formation. Isotopic mass balance calculations suggest that the decrease in δ15N during the Shuram excursion is best explained by the reduction of isotopic fractionation associated with water column denitrification (εwd) in response to feedbacks between carbon and nitrogen cycling, which were modulated by changes in primary productivity and recycled nutrient elements through remineralization of organic matter. The study presented here thus offers a new perspective for coupled variations in carbon and nitrogen cycles and sheds new light on this critical time in Earth history.Publisher PDFPeer reviewe

Learning a Consensus Sub-Network with Polarization Regularization and One Pass Training

The subject of green AI has been gaining attention within the deep learning community given the recent trend of ever larger and more complex neural network models. Existing solutions for reducing the computational load of training at inference time usually involve pruning the network parameters. Pruning schemes often create extra overhead either by iterative training and fine-tuning for static pruning or repeated computation of a dynamic pruning graph. We propose a new parameter pruning strategy for learning a lighter-weight sub-network that minimizes the energy cost while maintaining comparable performance to the fully parameterised network on given downstream tasks. Our proposed pruning scheme is green-oriented, as it only requires a one-off training to discover the optimal static sub-networks by dynamic pruning methods. The pruning scheme consists of a binary gating module and a novel loss function to uncover sub-networks with user-defined sparsity. Our method enables pruning and training simultaneously, which saves energy in both the training and inference phases and avoids extra computational overhead from gating modules at inference time. Our results on CIFAR-10 and CIFAR-100 suggest that our scheme can remove 50% of connections in deep networks with less than 1% reduction in classification accuracy. Compared to other related pruning methods, our method demonstrates a lower drop in accuracy for equivalent reductions in computational cost

Optimised Power Error Comparison Strategy for Direct Power Control of the Open-winding Brushless Doubly-Fed Wind Power Generator

This paper presents the conceptual analysis and comparative simulation and experimental evaluation of a novel power error comparison direct power control (PEC-DPC) strategy of the open-winding brushless doubly-fed reluctance generator (OW-BDFRG) for wind energy conversion systems (WECSs). As one of the promising candidates for limited speed range application of pump-alike and wind turbine with partially-rated converter. The emerging OW-BDFRG employed for the proposed PEC-DPC is fed via dual low-cost two-level converters, while the DPC concept is derived from the fundamental dynamic analyses between the calculated and controllable electrical power and flux of the BDFRG with two stators measurable voltage and current. Compared to the traditional two-level and three-level converter systems, the OW-BDFRG requires lower rated capacity of power devices and switching frequency converter, though have more flexible switching mode, higher reliability, redundancy and fault tolerance capability. The performance correctness and effectiveness of the proposed DPC strategy with the selected and optimised switching vector scheme are evaluated and confirmed through computer simulation studies and experimental measurements on a 25 kW generator test rig