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Your search keyword '"Ye, Yujian"' showing total 93 results
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93 results on '"Ye, Yujian"'

1. Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing

Author

Zhu, Shuyang, Zhu, Ziqing, Zhu, Linghua, Ye, Yujian, Bu, Siqi, and Djokic, Sasa Z.

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

The analysis of decision-making process in electricity markets is crucial for understanding and resolving issues related to market manipulation and reduced social welfare. Traditional Multi-Agent Reinforcement Learning (MARL) method can model decision-making of generation companies (GENCOs), but faces challenges due to uncertainties in policy functions, reward functions, and inter-agent interactions. Quantum computing offers a promising solution to resolve these uncertainties, and this paper introduces the Quantum Multi-Agent Deep Q-Network (Q-MADQN) method, which integrates variational quantum circuits into the traditional MARL framework. The main contributions of the paper are: identifying the correspondence between market uncertainties and quantum properties, proposing the Q-MADQN algorithm for simulating electricity market bidding, and demonstrating that Q-MADQN allows for a more thorough exploration and simulates more potential bidding strategies of profit-oriented GENCOs, compared to conventional methods, without compromising computational efficiency. The proposed method is illustrated on IEEE 30-bus test network, confirming that it offers a more accurate model for simulating complex market dynamics., Comment: 3 pages, 3 figures, plan for submitting to IEEE Power Engineering Letters

Published
2024

4. The effects of feelings of awe on the relationship between consumers' narcissism and impulsive consumption behaviors: A mediated moderation model

Author

Zhang, Mengwei, Li, Li, Ye, Yujian, Yu, Shiyi, and Zhong, Jianan

Subjects
Narcissism -- Research, Psychological research, Consumer behavior -- Research, Impulse buying -- Research, Psychology and mental health
Abstract

The feeling of awe is a complicated, mixed emotion that integrates confusion, astonishment, admiration and submission. Researchers have found beneficial effects of awe on individuals' social lives. This work aims to explore the impeding effect of awe on the relationship between narcissism and irrational consumption by performing 4 studies. After study 1 verified that narcissism was positively related to impulsive consumption, study 2 activated participants' feelings of awe through video clips and proved that awe reduced the effect of narcissism on impulsive consumption. Study 3 showed that the reduction in impulsive tendency was caused by awe rather than priming materials, and eliciting awe did not change an individual's sense of control. Study 4 involved an online experiment among office workers and revealed that the moderating effect of awe was fully mediated by self-focused attention. Overall, the mediated moderation model is verified. This study enriches the theoretical research of awe in the Chinese cultural context and provides practical meanings., Author(s): Mengwei Zhang [sup.1] [sup.2] , Li Li [sup.3] , Yujian Ye [sup.4] , Shiyi Yu [sup.1] , Jianan Zhong [sup.1] Author Affiliations: (1) Law and Politics School, Zhejiang University [...]

Published
2023
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20. Fast Resilience Assessment of Integrated Electricity-Gas Systems Considering Non-Convex Gas Flow Dynamics

Author

Liu, Pengxiang, Wu, Zhi, Gu, Wei, Lu, Yuping, Sun, Qirun, Ye, Yujian, and Strbac, Goran

Abstract

This paper proposes a novel spatial branch-and-cut approach for fast resilience analysis of integrated electricity-gas systems. The proposed method makes two improvements to the traditional branch-and-cut algorithms to accelerate the solution process of the scenario-based post-contingency load restoration. First, a new cone-specific spatial branching strategy is developed to handle the non-convex nature of the integrated electricity-gas flow. Compared with the existing branching strategies which can only handle bilinear equality constraints, the scale of branching variables is reduced by two-thirds without affecting the accuracy of solutions. Second, a combinatorial cut generation strategy is proposed to convert the traversal of all failure scenarios into a guided generation of single branch-and-cut search tree. The key idea is to equate the solution process of each failure scenario to a subset of the search tree, so that the load restoration problem only needs to be solved once. The performance of the proposed approach is tested on three integrated electricity-gas systems of different sizes. The comparison against seven alternative solution methods further verifies the superiority of the proposed method in terms of accuracy and efficiency.

Published
2024
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21. Towards Risk-Aware Real-Time Security Constrained Economic Dispatch: A Tailored Deep Reinforcement Learning Approach

Author

Hu, Jianxiong, Ye, Yujian, Tang, Yi, and Strbac, Goran

Abstract

In the presence of increasing uncertainties brought by intermittent renewable energy sources, security and risk management continue to be the most critical concern in modern power system operation. Risk-aware, real-time security constrained economic dispatch (RT-SCED) provides an efficient solution towards promptly, economically and robustly responding to the changes in the power system operating state. Despite different model-based methods have been developed to handle uncertainties, significant computation burden arise to incorporate $N$-1 contingency constraints with a higher temporal resolution in RT-SCED. Driven by similar computational challenges, risk evaluation is often overlooked in the current application of deep reinforcement learning (DRL) based data-driven methods in RT-SCED. This article proposes a DRL-based risk-aware RT-SCED methodological framework by incorporating a novel data-driven risk evaluation model to foster efficient agent-environment interactions. The real-time dispatch policies are constructed with an improved twin delayed deep deterministic policy gradient method. The policy network features a residual network architecture and incorporates an active power allocation mechanism to integrate empirical dispatch knowledge, preventing early termination and fostering more efficient learning behavior. Case studies validate the superior performance of the proposed method in risk-aware RT-SCED on cost efficiency, uncertainty adaptability and computational efficiency, through benchmarking against model-based and data-driven baseline methods.

Published
2024
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42. Capacity evaluation of central air conditioning load participating in peak shaving of renewable dominated power systems.

Author

Zhang, Rui, Yu, Jilai, Tang, Yi, and Ye, Yujian

Subjects
AIR conditioning, SHAVING, PEAK load, TIME management, CONSUMER behavior
Abstract

The development of renewable‐dominated power systems (RDPS) in the context of pursuing deep decarbonisation targets necessitates suitable exploitation of the peak shaving capacity (PSC) of central air conditioning loads (CACL) in multiple time scales. Based on the evaluation framework of CACL participating in the RDPS PSC and the CAC apparent temperature model and its operation mode, the CACL PSC evaluation model related to the resource allocation in multiple time scales is proposed. For the time scale of online dispatching, the capacity evaluation model for rapid CACL response to power system emergency peak shaving command and its continuous response potential is constructed. The method of optimising CACL PSC based on the load aggregator model is developed. For the intraday pre‐scheduling time scale, the evaluation model of CACL's remaining capacity after undertaking the peak shaving task and the ability to respond continuously to the peak shaving command is constructed. For the day‐ahead dispatching time scale, the PSC of CACL under different weather conditions, temperatures, holidays, and other typical scenarios is evaluated. Finally, case studies carried out on 14 central air‐conditioned buildings in Shanghai city comprehensively validate the effectiveness of the proposed CACL PSC evaluation model. [ABSTRACT FROM AUTHOR]

Published
2023
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