Browsing by Author "Li, Yang"
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Item Open Access Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response(Elsevier, 2023-02-04) Li, Yang; Han, Meng; Shahidehpour, Mohammad; Li, Jiazheng; Long, ChaoA community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and ∞-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.Item Open Access Editorial: New theories, models, and AI methods of brain dynamics, brain decoding and neuromodulation(Frontiers, 2023-12-12) Guo, Yuzhu; Li, Yang; Wei, Hua-Liang; Zhao, YifanThe human brain is highly dynamic and complex, supporting a remarkable range of functions by dynamically integrating and coordinating different brain regions and networks across multiple spatial and temporal scales. Research on the human brain has become truly interdisciplinary involving medicine, neurobiology, engineering, and related fields. A thorough understanding of the mechanisms of neuromodulation actions is urgently needed for stimulation parameters optimization, response prediction, and consistent therapy. This Research Topic aims to combine top-down and bottom-up methods to produce robust results that allow for a meaningful interpretation in terms of the underlying brain dynamics with an emphasis on brain decoding and neuromodulation.Item Open Access Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: a deep reinforcement learning approach(Elsevier, 2022-12-29) Li, Yang; Bu, Fanjin; Li, Yuanzheng; Long, ChaoMulti-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of “hydrothermal simultaneous transmission” (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit’s output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater.Item Open Access Retrieving back plastic wastes for conversion to value added petrochemicals: opportunities, challenges and outlooks(Elsevier, 2023-06-01) Kumar, Manish; Bolan, Shiv; Padhye, Lokesh P.; Konarova, Muxina; Foong, Shin Ying; Lam, Su Shiung; Wagland, Stuart T.; Cao, Runzi; Li, Yang; Batalha, Nuno; Ahmed, Mohamed; Pandey, Ashok; Siddique, Kadambot H.M.; Wang, Hailong; Rinklebe, Jörg; Bolan, NanthiPlastic production and its unplanned management and disposal, has been shown to pollute terrestrial, aquatic, and atmospheric environments. Petroleum-derived plastics do not decompose and tend to persist in the surrounding environment for longer time. Plastics can be ingested and accumulate into the tissues of both terrestrial and aquatic animals, which can impede their growth and development. Petrochemicals are the primary feedstocks for the manufacture of plastics. The plastic wastes can be retrieved back for conversion to value added petrochemicals including aromatic char, hydrogen, synthesis gas, and bio-crude oil using various technologies including thermochemical, catalytic conversion and chemolysis. This review focusses on technologies, opportunities, challenges and outlooks of retrieving back plastic wastes for conversion to value added petrochemicals. The review also explores both the technical and management approaches for conversion of plastic wastes to petrochemicals in regard to commercial feasibility, and economic and environmental sustainability. Further, this review work provides a detailed discussion on opportunities and challenges associated with recent thermochemical and catalytic conversion technologies adopted for retrieving plastic waste to fuels and chemicals. The review also recommends prospects for future research to improve the processes and cost-efficiency of promising technologies for conversion of plastic wastes to petrochemicals. It is envisioned that this review would overcomes the knowledge gaps on conversion technologies and further contribute in emerging sustainable approaches for exploiting plastic wastes for value-added products.Item Open Access Special issue on innovative methods and techniques for power and energy systems with high penetration of distributed energy resources [Editorial](Elsevier, 2023-10-25) Li, Yang; Lei, Shunbo; Chen, Xia; Long, Chao; Zhou, Yifan; Kim, Young-Jin1. Background The contemporary landscape of power and energy systems (P&ESs) is experiencing a significant transformation, marked by the integration of distributed energy resources (DERs) like solar photovoltaics, wind turbines, energy storage systems, and electric vehicles. Although these DERs bring forth myriad benefits, they also introduce challenges in variability management, uncertainty, and cyber vulnerabilities. This special issue of Energy Reports offers a comprehensive perspective on these intertwined challenges and opportunities.Item Open Access Wind power forecasting considering data privacy protection: a federated deep reinforcement learning approach(Elsevier, 2022-11-16) Li, Yang; Wang, Ruinong; Li, Yuanzheng; Zhang, Meng; Long, ChaoIn a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy-preserving and data islands problem. To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL). Firstly, this paper uses the deep deterministic policy gradient (DDPG) algorithm as the basic forecasting model to improve prediction accuracy. Secondly, we integrate the DDPG forecasting model into the framework of federated learning. The designed FedDRL can obtain an accurate prediction model in a decentralized way by sharing model parameters instead of sharing private data which can avoid sensitive privacy issues. The simulation results show that the proposed FedDRL outperforms the traditional prediction methods in terms of forecasting accuracy. More importantly, while ensuring the forecasting performance, FedDRL can effectively protect the data privacy and relieve the communication pressure compared with the traditional centralized forecasting method. In addition, a simulation with different federated learning parameters is conducted to confirm the robustness of the proposed scheme.